Sec2_VRP.ipynb

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    "# Quantum Supply Chain Manager\n",
    "\n",
    "The quantum supply chain manager is a quantum solution for logistics problems. We use the power of quantum machine learning for product [backorder](https://www.investopedia.com/terms/b/backorder.asp) prediction and quantum optimization for finding the best route to pick those products with high demand to store them in strategic warehouses. With the first technique, our clients can be prepared for increasing the production of their products when they are in high demand. Once we have established a set of products needed during a period of time, we use our second solution, the vehicle routing problem (VRP) solution to find the optimal route for picking these products. This reduces considerably costs associated with logistics, transportation, backorders, and overstocking for our clients. In summary, these solutions will improve business in terms of client satisfaction, backorder and shipping transportation costs.\n",
    "\n",
    "<img src=\"./Images/QSCM.png\" width=900>\n",
    "\n",
    "### But, why this is important?\n",
    "\n",
    "Mainly, backorders indicate that the demand for a specific product was not well understood at a certain point in time and needs to be reevaluated. This has a high impact on the customers' loyalty and the company revenue. The solution to this problem must be increasing the production of the products with a high probability of backorder before that happen avoiding overstocking them which may result in high inventory costs. Using quantum neural networks (QNN), we can improve the predictability, the training size, and the number of variables for the backorder predictions.\n",
    "\n",
    "On the other hand, the VRP finds optimal solutions for picking products in terms of costs and time. The benefit of finding an optimal solution can represent a saving of up to 30% of the costs associated with transportation [1]. However, current techniques deal with up to 400 variables in a reasonable time. Soon with a growing e-market, the capabilities of such models will be overcome. In the near future, we will need systems able to solve thousands of variables in small periods of time. \n",
    "\n",
    "### Is this something that we cannot do with classical computation?\n",
    "\n",
    "The short answer is \"depends\", we can rely on the power of classical computation and heuristic methods to solve such a problem at **small scale**. At this moment, classical algorithms have the capacity of working with hundreds of variables to solve the VRP in a couple of minutes with a good quality compared with the best-known solutions (BKS) [2]. This means in small scenarios, this is a viable solution. However, as the e-market is expanding worldwide the requirement will be that of solving thousands of variables with good quality and in a reasonable time. The increment in variables number has a tendency to increase exponentially the time required to solve these problems on classical computers. This is an aspect that quantum computers can overcome, encoding large problems in a reasonable time and solving them much faster with quantum algorithms such as the quantum approximate optimization algorithm (QAOA).\n",
    "\n",
    "We foresee a future where quantum computers will reduce largely the costs associated with storage and transportation at a big scale thanks to the combination of forecasting backorders and route optimization.\n",
    "\n",
    "## Proof of Concept\n",
    "\n",
    "To show the applicability of our concept, we have used real data of some products in the [dataset backorder](https://github.com/akhiilkasare/Back-Order-Prediction-iNeuron). The dataset contains 23 variables including:\n",
    "\n",
    "|     |     |     | | | |\n",
    "| --- | --- | --- | - | - | - |\n",
    "| **sku** Product ID | **national_inv** Current inventory level for the part |**lead_time** Transit time for product | **in_transit_qty** Amount of product in transit from source|**forecast_3_month** Forecast sales for the next 3 months | **forecast_6_month** Forecast sales for the next 6 months|\n",
    "|**forecast_9_month** Forecast sales for the next 9 months|**sales_1_month** Sales quantity for the prior 1 month time period|**sales_3_month** Sales quantity for the prior 3 month time period |**sales_6_month** Sales quantity for the prior 6 month time period |**sales_9_month** Sales quantity for the prior 9 month time period |**min_bank** Minimum recommend amount to stock \n",
    "|**potential_issue** Source issue for part identified |**pieces_past_due** Parts overdue from source |**perf_6_month_avg** Source performance for prior 6 month period |**perf_12_month_avg** Source performance for prior 12 month period |**local_bo_qty** Amount of stock orders overdue|**deck_risk** Part risk flag\n",
    "|**oe_constraint** Part risk flag | **ppap_risk** Part risk flag | **stop_auto_buy** Part risk flag | **rev_stop** Part risk flag| **went_on_backorder** – Product actually went on backorder. <font color='green'>This is the target value</font>.\n",
    "\n",
    "\n",
    "The dataset contains 1687861 products of some companies with a portion of 11293 that went backorder. We take 2000 cases equally distributed between <font color='green'>True</font> and <font color='red'>False</font> backorders from the dataset to do the training of our QNN. Once, we can predict if a product is backorder we select a small dataset of backorder products to make an optimization using the VRP with QAOA and VQE to select the optimal route. The comparison of the VRP results are contrasted with those of [docplex](https://pypi.org/project/docplex/) a classical optimizer. \n",
    "\n",
    "# Outline\n",
    "\n",
    "1. Backorder prediction using a QNN\n",
    "\n",
    "    1.1 Setting the problem\n",
    "    1.2 The Ansatz encoding\n",
    "    1.3 Different ansatz solutions\n",
    "\n",
    "2. Vehicle routing problem solution.\n",
    "\n",
    "    2.1 Setting the problem \n",
    "    2.2 The QUBO representation of the vehicle routing problem \n",
    "    \n",
    "        2.2.1 Classical solution of the QUBO model\n",
    "        2.2.2 Quantum Solution of the QUBO model\n",
    "3. Conclusion and Future Work\n",
    "\n",
    "\n",
    "4. References\n",
    "\n",
    "# 1. Backorder Prediction\n",
    "\n",
    "This is about an order that cannot be fulfilled at a given time because there is not enough inventory or the item is out of stock, but you can guarantee delivery of the product in the future. Unlike the out-of-stock situation, in the backorder situation customers can purchase the items and place the order as they will be guaranteed a delivery in the future, i.e., it is an order with a delayed delivery date.\n",
    "\n",
    "## 1.1 Setting the problem\n",
    "\n",
    "- From the dataset of backorders, we analyze different size data model with a special case with 2000 sampes for training and 6159 for validation. This will simulate a case were a lack of results is presented.\n",
    "\n",
    "- Once the dataset is selected, 3 ansatz architectures for the QNN are analized multiscale entanglement renormalization ansatz (MERA) [3], tree tensor networks TTN [4], and matrix product state (MPS) [5]. \n",
    "\n",
    "- Next, the results of the QNN are compared against a classical neural network.\n",
    "\n",
    "## 1.2 The ansatz encoding\n",
    "\n",
    "For the quantum computing case, the following scheme is generated for each experiment and it has 3 different ansatz based on 3 tensor networks (TN) MERA, TTN, and MPS. With  only 10 variables we designed the amplitud embedding an one layer for each TN. The figure below shows the different ansatz choosed for the prediction of backorder.\n",
    "\n",
    "<img src=\"./Images/tensor.png\" width=800>\n",
    "\n",
    "## 1.3 Different ansatz solutions\n",
    "\n",
    "For the three different ansatzes presented in Sec 1.2, different architectures were tested. For the case of the MERA architecture 5 different architectures were trained, 1 Layer, 2 Layers, 4 Layers, 1 Layer with a standard scaler, and 1 layer with depolarizing noise of 0.2. For the case of MPS and TTN the architectures tested were 1 layer, 2 layers, and 4 layers. The results were compared against a classical neural network with 640 parameters. From all the architectures tested the best result was obtained with MERA and 1 layer. This architecture outperforms the best result with a classical neural network in almost all the different metrics presented in the table below. Most important, MERA 1 layer is able to reduce false positives which can result in overstocking. Another good point is that even with some noise the 1 layer model is able to give good results in terms of prediction as is shown in the confusion matrices shown below.\n",
    "\n",
    "<img src=\"./Images/Results-Ansatz.png\" width=800>\n",
    "\n",
    "\n",
    "\n",
    "# 2. Vehicle routing problem Solution\n",
    "\n",
    "Once we have the solution of the backorders predictions, we select some of them to do an optimization of the best route giving a set of *K* trucks using the Vehicle routing problem (VRP)\n",
    "\n",
    "\n",
    "## 2.1 Setting the problem \n",
    "\n",
    "- We take a subset of 10 companies that were predicted to have backorder of some products. \n",
    "- From the warehouse, where we collect all the products, and each company there is always a connection.\n",
    "- 3 trucks are chosen to collect the products. \n",
    "- Connections between companies are selected if the Euclidian distance is less than a threshold\n",
    "\n",
    "<img src=\"./Images/SettingTheProblem.png\" width=800>\n",
    "\n",
    "## 2.2 The QUBO representation of the vehicle routing problem \n",
    "\n",
    "To encode a problem as the Vehicle Routing Problem on a quantum computer it is needed to represent it as a quadratic unconstrained binary optimization ([QUBO](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization)) problem. In the QUBO representation, the cost function has linear and cuadratic terms and the constrains of the objective function are added as penalization to the cost function. The following function. In the Figure below, we are showing the objective function of the VRP:\n",
    "\n",
    "<img src=\"./Images/CostFunc.png\" width=800>\n",
    "\n",
    "The terms of the above equations are:\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "- C<sub>ij</sub> : We consider the cost associated with a specific route ij as a combination of distance, traffic, and other forecasted terms related to the disadvantages of taking the particular route on a specific day. \n",
    "\n",
    "- K: We find the solution for 3 trucks.\n",
    "</div>\n",
    "\n",
    "### 2.2.1 Classical solution of the QUBO model\n",
    "\n",
    "Using docplex, the python version of the optimization solver **cplex**, we obtain the result of the problem explained above. The Figure below shows the graph representation of the solution, where the edges observed are the connections between companies that minimize the cost function and satisfy all the constraints. This solution will be used in the rest of the notebook as the optimal solution.\n",
    "\n",
    "<img src=\"./Images/OptimalSolution.png\" width=400>\n",
    "\n",
    "### 2.2.2 Quantum Solution of the QUBO model\n",
    "\n",
    "Once we have the quadratic program of our model. We can use the qiskit optimization library to translate it into its QUBO representation. In this case, we have inequality constraints that in most cases need extra variables, known as **slack variables**. For this specific case, the number of variables increases from  68 to 196 to represent the inequality constraints. \n",
    "\n",
    "A further constraint is the number of qubits we can simulate. Unless we have a supercomputer, the best option to simulate programos with more than 20 qubits is using **qasm_simulator** from IBM in **runtime mode**. The maximum number of qubits we can simulate using the qasm_simulator is 32. For the case of real devices, we have a similar problem, we can only run experiments on a 7 qubits device (ibm_oslo, ibm_lagos) to which we have access.\n",
    "\n",
    "Therefore, using the previous solution we can reduce the number of variables choosing the optimal solution for the **slack variables**. For the variables that represent the routes of company i to company j we select the optimal for some of them and leave the quantum computer find the optimal solution for the rest in two cases:\n",
    "\n",
    "1) Using 7 variables and replace the orhers with the optimal solution. From the 7 variables left, 3 have a optimal solution equal to 1 and 4 equal to 0 (testing local machine and ibm_lagos **runtime**).\n",
    "\n",
    "2) Using 15 variables and replace the others with the optimal solution. From the variables left 5 have a optimal solution equal to 1 and 10 equal to 0 (testing qasm_simulator **runtime**).\n",
    "\n",
    "<img src=\"./Images/ProblemSolutions.png\" width=600>\n",
    "\n",
    "#### 7 Qubits Results\n",
    "\n",
    "We tested different models to see the applicability of the current model with ideal solutions using two classical optimizer ([SPSA](https://en.wikipedia.org/wiki/Simultaneous_perturbation_stochastic_approximation) and [COBYLA](https://qiskit.org/documentation/stubs/qiskit.algorithms.optimizers.COBYLA.html#:~:text=COBYLA%20is%20a%20numerical%20optimization,scipy.optimize.minimize%20COBYLA.)) for two quantum algorithms ([QAOA](https://qiskit.org/textbook/ch-applications/qaoa.html) with 2 layer repetitions and [VQE](https://pennylane.ai/qml/demos/tutorial_vqe.html) with ansatz [TwoLocal](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html), number of shots=1024 for both cases). Additionally, we run our model in *ibm_lagos* using [qiskit runtime](https://quantum-computing.ibm.com/lab/docs/iql/runtime/) with mitigated and unmitigated results with the same settings. The figure below shows the results for the different cases, we see that all the simulations reach the ideal solution shown in the graphs on the left, while the solution for the ibm_oslo and ibm_lagos experiments using COBYLA tries to go to the optimal solution, even though the noise is affecting them (Further experiments are needed to draw conclusion because at this point the max iterations were kept small).\n",
    "\n",
    "<img src=\"./Images/Sol7Qprint2.png\" width=1000>\n",
    "\n",
    "#### 15 Qubits Results\n",
    "\n",
    "For the case of 15 qubits, the solution includes only QAOA (with 3 layers repetitions) and VQE (with [TwoLocal ansatz](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html)) with the classic optimizer COBYLA. The results were obtained from qiskit runtime and the ideal simulator ibmq_qasm_simulator. As it is shown in the figure below the QAOA algorithm does not reach the solution while VQE can reach the solution. \n",
    "\n",
    "<img src=\"./Images/Sol15Q.png\" width=1000>\n",
    "\n",
    "# 3. Conclusion and Future Work\n",
    "\n",
    "\n",
    "# 3.2 Technical conclusions\n",
    "\n",
    "- The current devices as *ibm_lagos* is still too noisy to get back correct results with real devices for a 7 qubits problem using VQE.\n",
    "\n",
    "- It is important for the QUBO representation to choose a good penalty parameter when converting from a docplex model. The default penalty multiplier (similar to the Lagrange multiplier) for the equality and inequality constraints makes the penalty too large compared with the objective function. Therefore, the solver will lose soon the optimal direction. We have to set the penalty manually with a value of 15.\n",
    "\n",
    "- When planning for the objectives of this project. We wanted to make a simulation of the QAOA with 25 qubits, 3 repetitions, and COBYLA optimizer with max iterations of 100 using ibmq_qasm_simulator. Soon we realized we were overoptimistic. The maximum time we can simulate continuously in runtime mode is  14400 seconds and an iteration using COBYLA, 1 repetition of the QAOA and 20 qubits takes 700 seconds. Therefore, we have technically 20 iterations with that setting. We decided to downgrade our model to work with 15 qubits, 2 repetitions, and 100 max iterations.\n",
    "\n",
    "\n",
    "# 4. References\n",
    "\n",
    "[1] Psaraftis, H.N. (1988). Vehicle Routing: Methods and Studies. 16: 223–248.\n",
    "\n",
    "[2] Tan, S., & Yeh, W. (2021). applied sciences The Vehicle Routing Problem : State-of-the-Art Classification and Review. Applied Sciences, 11.\n",
    "\n",
    "[3] Cong, I., Choi, S., & Lukin, M. D. (2019). Quantum convolutional neural networks. Nature Physics, 15(12), 1273–1278. \n",
    "\n",
    "[4] Cheng, S., Wang, L., Xiang, T., & Zhang, P. (2019). Tree tensor networks for generative modeling. Physical Review B, 99(15), 1–10. https://doi.org/10.1103/PhysRevB.99.155131\n",
    "\n",
    "[5] Ran, S. J. (2020). Encoding of matrix product states into quantum circuits of one- A nd two-qubit gates. Physical Review A, 101(3), 1–7. https://doi.org/10.1103/PhysRevA.101.032310"
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   "source": [
    "#### Required libraries"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35985a5f",
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   "outputs": [],
   "source": [
    "# %pip install qiskit \n",
    "# %pip install docplex\n",
    "# %pip install qiskit_optimization\n",
    "# %pip install networkx\n",
    "# %pip install geopandas\n",
    "# %pip install folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96211d4f",
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   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import geopandas as gpd\n",
    "import folium\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import time\n",
    "import random\n",
    "from IPython.display import display\n",
    "\n",
    "from qiskit import IBMQ\n",
    "from qiskit.algorithms import QAOA, VQE\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit.algorithms.optimizers import COBYLA, SPSA # Classical simulator\n",
    "from qiskit.providers.aer import AerSimulator\n",
    "\n",
    "from docplex.mp.model import Model\n",
    "from qiskit_optimization.runtime import QAOAClient, VQEClient\n",
    "from qiskit_optimization.converters import QuadraticProgramToQubo \n",
    "from qiskit_optimization.translators import from_docplex_mp\n",
    "from qiskit_optimization.algorithms import CplexOptimizer, MinimumEigenOptimizer"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Setting the problem \n",
    "\n",
    "- We take a subset of 10 companies that were predicted to have backorder of some products. \n",
    "- From the warehouse, where we collect all the products, and each company there is always a connection.\n",
    "- 3 trucks are chosen to collect the products. \n",
    "- Connections between companies were selected randomly to get an additional of 14 connections. \n",
    "- In terms of quantum computing resources this means 29 qubits because each connection means a qubit.\n"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "id": "3fce7767",
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   "outputs": [],
   "source": [
    "path = gpd.datasets.get_path('nybb')\n",
    "df = gpd.read_file(path)"
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   "source": [
    "### Visualization on the map of the different companies from where we will be collecting the products"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "f934ae81",
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       "    \n",
       "        var popup_1a05f85ab13e8feb6aa69745c1df30c8 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_48a935d7bad98337b80d2f2945e9cd3e = $(`&lt;div id=&quot;html_48a935d7bad98337b80d2f2945e9cd3e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 1&lt;/div&gt;`)[0];\n",
       "            popup_1a05f85ab13e8feb6aa69745c1df30c8.setContent(html_48a935d7bad98337b80d2f2945e9cd3e);\n",
       "        \n",
       "\n",
       "        marker_d57a38d7ce2a16407f43de8077480e40.bindPopup(popup_1a05f85ab13e8feb6aa69745c1df30c8)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7b8e9e264565fa7f9eb7a6c195fdf2ba = L.marker(\n",
       "                [40.9031710333525, -73.76462386279145],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_6b8825d491f27a8f53a8be267c74c2e7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_0e9a5324d5a7e0fedc9259c3dc3945a0 = $(`&lt;div id=&quot;html_0e9a5324d5a7e0fedc9259c3dc3945a0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 2&lt;/div&gt;`)[0];\n",
       "            popup_6b8825d491f27a8f53a8be267c74c2e7.setContent(html_0e9a5324d5a7e0fedc9259c3dc3945a0);\n",
       "        \n",
       "\n",
       "        marker_7b8e9e264565fa7f9eb7a6c195fdf2ba.bindPopup(popup_6b8825d491f27a8f53a8be267c74c2e7)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_17d3d46bff5a5f18137f7833d424df36 = L.marker(\n",
       "                [40.807797223685164, -73.81126833649652],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_0507928c7b033ba20d5930c070ff9727 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_0505ca5b9d4d159e95e67af44aedcee1 = $(`&lt;div id=&quot;html_0505ca5b9d4d159e95e67af44aedcee1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 3&lt;/div&gt;`)[0];\n",
       "            popup_0507928c7b033ba20d5930c070ff9727.setContent(html_0505ca5b9d4d159e95e67af44aedcee1);\n",
       "        \n",
       "\n",
       "        marker_17d3d46bff5a5f18137f7833d424df36.bindPopup(popup_0507928c7b033ba20d5930c070ff9727)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_839b9fa3b6ee31d5cc33af096b126760 = L.marker(\n",
       "                [40.69840825044273, -73.89769263821798],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_0ce42a3747d7894431b40e34dad22a55 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_411820f3daf075690379caa919612f35 = $(`&lt;div id=&quot;html_411820f3daf075690379caa919612f35&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 4&lt;/div&gt;`)[0];\n",
       "            popup_0ce42a3747d7894431b40e34dad22a55.setContent(html_411820f3daf075690379caa919612f35);\n",
       "        \n",
       "\n",
       "        marker_839b9fa3b6ee31d5cc33af096b126760.bindPopup(popup_0ce42a3747d7894431b40e34dad22a55)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_fdc6597294046d7fc8402c03bf39b230 = L.marker(\n",
       "                [40.78056645364654, -73.86730698469009],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_ea19c0352adfe7c7d5f8c1ed65e3b458 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_7024f8af8ae48cfce57d28ff724bfb3a = $(`&lt;div id=&quot;html_7024f8af8ae48cfce57d28ff724bfb3a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 5&lt;/div&gt;`)[0];\n",
       "            popup_ea19c0352adfe7c7d5f8c1ed65e3b458.setContent(html_7024f8af8ae48cfce57d28ff724bfb3a);\n",
       "        \n",
       "\n",
       "        marker_fdc6597294046d7fc8402c03bf39b230.bindPopup(popup_ea19c0352adfe7c7d5f8c1ed65e3b458)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_9b8f1f720d8275ca6a38466183b0508c = L.marker(\n",
       "                [40.853437610962374, -73.96059664398177],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_7755849126dbac2c625317d0ad186df9 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_f1bdaad71049f9e92f0319e85cb0c405 = $(`&lt;div id=&quot;html_f1bdaad71049f9e92f0319e85cb0c405&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 6&lt;/div&gt;`)[0];\n",
       "            popup_7755849126dbac2c625317d0ad186df9.setContent(html_f1bdaad71049f9e92f0319e85cb0c405);\n",
       "        \n",
       "\n",
       "        marker_9b8f1f720d8275ca6a38466183b0508c.bindPopup(popup_7755849126dbac2c625317d0ad186df9)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_80186ec3ba5188dc00a84f4b5a80503f = L.marker(\n",
       "                [40.834878504194265, -73.8762309259004],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_0e2ef1311a7be9c1d822dcb32338dab7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_305bd61c09cd315bf14362b58a3b1aa0 = $(`&lt;div id=&quot;html_305bd61c09cd315bf14362b58a3b1aa0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 7&lt;/div&gt;`)[0];\n",
       "            popup_0e2ef1311a7be9c1d822dcb32338dab7.setContent(html_305bd61c09cd315bf14362b58a3b1aa0);\n",
       "        \n",
       "\n",
       "        marker_80186ec3ba5188dc00a84f4b5a80503f.bindPopup(popup_0e2ef1311a7be9c1d822dcb32338dab7)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_dc3875d5f103e09330855beb6d66b1cb = L.marker(\n",
       "                [40.895296782430805, -73.82131451197903],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_f445b164a1d72c3928ca1a2ef25e4cdf = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_79ec05b100a64615cf74c559ab2a32e4 = $(`&lt;div id=&quot;html_79ec05b100a64615cf74c559ab2a32e4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 8&lt;/div&gt;`)[0];\n",
       "            popup_f445b164a1d72c3928ca1a2ef25e4cdf.setContent(html_79ec05b100a64615cf74c559ab2a32e4);\n",
       "        \n",
       "\n",
       "        marker_dc3875d5f103e09330855beb6d66b1cb.bindPopup(popup_f445b164a1d72c3928ca1a2ef25e4cdf)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_c1f8d855db134cf43b2cbadfe763da8b = L.marker(\n",
       "                [41.03886091565263, -73.86403698540576],\n",
       "                {}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_b1a5354eb799df2eccea21543b702fe1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_7eedca88acfbd052a0cb5a6c1674db80 = $(`&lt;div id=&quot;html_7eedca88acfbd052a0cb5a6c1674db80&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Id: 9&lt;/div&gt;`)[0];\n",
       "            popup_b1a5354eb799df2eccea21543b702fe1.setContent(html_7eedca88acfbd052a0cb5a6c1674db80);\n",
       "        \n",
       "\n",
       "        marker_c1f8d855db134cf43b2cbadfe763da8b.bindPopup(popup_b1a5354eb799df2eccea21543b702fe1)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_64eb51287745b426309e6e1f4e765f07 = L.circleMarker(\n",
       "                [40.80770544077999, -73.90195301293059],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;red&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 10, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_23ee97e5fcd8f01ee221dff59f2f9558);\n",
       "        \n",
       "    \n",
       "        var popup_a0f267836c2fd56087c7d92c5fb5a7d3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            var html_0988989348c110e69b464631bf9c0fcc = $(`&lt;div id=&quot;html_0988989348c110e69b464631bf9c0fcc&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;stong&gt;Warehouse&lt;/stong&gt;&lt;/div&gt;`)[0];\n",
       "            popup_a0f267836c2fd56087c7d92c5fb5a7d3.setContent(html_0988989348c110e69b464631bf9c0fcc);\n",
       "        \n",
       "\n",
       "        circle_marker_64eb51287745b426309e6e1f4e765f07.bindPopup(popup_a0f267836c2fd56087c7d92c5fb5a7d3)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fdfcd4cea00>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(location=[40.70, -73.94], zoom_start=10, max_zoom=12, tiles='CartoDB positron')\n",
    "    \n",
    "# Project to NAD83 projected crs\n",
    "df = df.to_crs(epsg=2263)\n",
    "\n",
    "# Access the centroid attribute of each polygon\n",
    "df['centroid'] = df.centroid\n",
    "\n",
    "# Project to WGS84 geographic crs\n",
    "\n",
    "# geometry (active) column\n",
    "df = df.to_crs(epsg=4326)\n",
    "\n",
    "# Centroid column\n",
    "df['centroid'] = df['centroid'].to_crs(epsg=4326)\n",
    "\n",
    "np.random.seed(7)\n",
    "locations = []\n",
    "ids = 0\n",
    "for _, r in df.iterrows():\n",
    "    lat, lon = r['centroid'].y, r['centroid'].x\n",
    "    for i in range(2):\n",
    "        lat_rand, lon_rand = lat + 0.2 * np.random.rand(), lon +0.1 * np.random.rand()\n",
    "        locations.append((lon_rand, lat_rand))\n",
    "        folium.Marker(location=[lat_rand, lon_rand], popup=f'Id: {ids}').add_to(m)\n",
    "        ids += 1\n",
    "center = np.array(locations).mean(axis=0)\n",
    "locations = [(center[0], center[1])] + locations\n",
    "folium.CircleMarker(location=[center[1], center[0]], radius=10, popup=\"<stong>Warehouse</stong>\",\n",
    "                    color=\"red\",fill=True, fillOpacity=1, fillColor=\"tab:red\").add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a57543e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing the results to produce a graph in Graphx\n",
    "companies = np.array(locations)\n",
    "companies -= companies[0]\n",
    "companies /= (np.max(np.abs(companies), axis=0))\n",
    "r = list(np.sqrt(np.sum(companies ** 2, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82ac9b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of edges of this problem is: 34\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 1 # Limit for to not consider the route from company i to j if the distance is larger than a threshold\n",
    "n_companies = len(companies)\n",
    "G = nx.Graph(name=\"VRP\")\n",
    "G.add_nodes_from(range(n_companies))\n",
    "# G.add_weighted_edges_from([0, i, w] for i, w in zip(range(1, n_companies), r[1:]))\n",
    "np.random.seed(2)\n",
    "count = 0\n",
    "for i in range(n_companies):\n",
    "    for j in range(n_companies):\n",
    "        if i != j:\n",
    "            rij = np.sqrt(np.sum((companies[i] - companies[j])**2))\n",
    "            if (rij < threshold) or (0 in [i, j]):\n",
    "                count +=1\n",
    "                G.add_weighted_edges_from([[i, j, rij]])\n",
    "                r.append(rij)\n",
    "colors = [plt.cm.get_cmap(\"coolwarm\")(x) for x in r[1:]]\n",
    "nx.draw(G, pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])\n",
    "print(f\"The number of edges of this problem is: {len(G.edges)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367dd5a7",
   "metadata": {},
   "source": [
    "### The QUBO of the vehicle routing problem \n",
    "\n",
    "To encode a problem as the Vehicle Routing Problem on a quantum computer it is needed to represent it as a quadratic unconstrained binary optimization (QUBO) problem. In the QUBO representation, the cost function has linear and cuadratic terms and the constrains are added as penalization to the cost function. The following function. In the Figure below, we are showing the objective function of the VRP:\n",
    "\n",
    "<img src=\"./Images/CostFunc.png\" width=800>\n",
    "\n",
    "The terms of the above equations are:\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "- C<sub>ij</sub> : We consider the cost associated with a specific route ij as a combination of distance, traffic, and other forecasted terms related to the disadvantages of taking the particular route on a specific day. \n",
    "\n",
    "- K: We find the solution for 2 trucks.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2753d7d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of qubits needed to solve the problem is: 68\n",
      "\\ This file has been generated by DOcplex\n",
      "\\ ENCODING=ISO-8859-1\n",
      "\\Problem name: VRP\n",
      "\n",
      "Minimize\n",
      " obj: 1.332623442714 x_0_1 + 1.332623442714 x_1_0 + 1.167267340961 x_0_2\n",
      "      + 1.167267340961 x_2_0 + 0.870924584994 x_0_3 + 0.870924584994 x_3_0\n",
      "      + 0.506337604554 x_0_4 + 0.506337604554 x_4_0 + 0.473427767721 x_0_5\n",
      "      + 0.473427767721 x_5_0 + 0.226286197585 x_0_6 + 0.226286197585 x_6_0\n",
      "      + 0.382564916313 x_0_7 + 0.382564916313 x_7_0 + 0.185594213224 x_0_8\n",
      "      + 0.185594213224 x_8_0 + 0.588478457259 x_0_9 + 0.588478457259 x_9_0\n",
      "      + 1.022163668163 x_0_10 + 1.022163668163 x_10_0 + 0.314878112966 x_1_2\n",
      "      + 0.314878112966 x_2_1 + 0.487918004849 x_3_4 + 0.487918004849 x_4_3\n",
      "      + 0.781044787250 x_3_6 + 0.781044787250 x_6_3 + 0.689644793506 x_3_8\n",
      "      + 0.689644793506 x_8_3 + 0.318359687012 x_3_9 + 0.318359687012 x_9_3\n",
      "      + 0.807887929520 x_3_10 + 0.807887929520 x_10_3 + 0.675868194806 x_4_5\n",
      "      + 0.675868194806 x_5_4 + 0.334333083016 x_4_6 + 0.334333083016 x_6_4\n",
      "      + 0.856833254405 x_4_7 + 0.856833254405 x_7_4 + 0.381169417109 x_4_8\n",
      "      + 0.381169417109 x_8_4 + 0.382664692182 x_4_9 + 0.382664692182 x_9_4\n",
      "      + 0.393840203853 x_5_6 + 0.393840203853 x_6_5 + 0.757072348368 x_5_7\n",
      "      + 0.757072348368 x_7_5 + 0.602421444816 x_5_8 + 0.602421444816 x_8_5\n",
      "      + 0.952553004353 x_5_9 + 0.952553004353 x_9_5 + 0.608850835621 x_6_7\n",
      "      + 0.608850835621 x_7_6 + 0.240184142440 x_6_8 + 0.240184142440 x_8_6\n",
      "      + 0.558831877720 x_6_9 + 0.558831877720 x_9_6 + 0.477848914445 x_7_8\n",
      "      + 0.477848914445 x_8_7 + 0.798486241303 x_7_9 + 0.798486241303 x_9_7\n",
      "      + 0.966504238519 x_7_10 + 0.966504238519 x_10_7 + 0.402909595455 x_8_9\n",
      "      + 0.402909595455 x_9_8 + 0.885069440098 x_8_10 + 0.885069440098 x_10_8\n",
      "      + 0.665305782026 x_9_10 + 0.665305782026 x_10_9\n",
      "Subject To\n",
      " c0: x_1_0 + x_1_2 = 1\n",
      " c1: x_2_0 + x_2_1 = 1\n",
      " c2: x_3_0 + x_3_4 + x_3_6 + x_3_8 + x_3_9 + x_3_10 = 1\n",
      " c3: x_4_0 + x_4_3 + x_4_5 + x_4_6 + x_4_7 + x_4_8 + x_4_9 = 1\n",
      " c4: x_5_0 + x_5_4 + x_5_6 + x_5_7 + x_5_8 + x_5_9 = 1\n",
      " c5: x_6_0 + x_6_3 + x_6_4 + x_6_5 + x_6_7 + x_6_8 + x_6_9 = 1\n",
      " c6: x_7_0 + x_7_4 + x_7_5 + x_7_6 + x_7_8 + x_7_9 + x_7_10 = 1\n",
      " c7: x_8_0 + x_8_3 + x_8_4 + x_8_5 + x_8_6 + x_8_7 + x_8_9 + x_8_10 = 1\n",
      " c8: x_9_0 + x_9_3 + x_9_4 + x_9_5 + x_9_6 + x_9_7 + x_9_8 + x_9_10 = 1\n",
      " c9: x_10_0 + x_10_3 + x_10_7 + x_10_8 + x_10_9 = 1\n",
      " c10: x_0_1 + x_2_1 = 1\n",
      " c11: x_0_2 + x_1_2 = 1\n",
      " c12: x_0_3 + x_4_3 + x_6_3 + x_8_3 + x_9_3 + x_10_3 = 1\n",
      " c13: x_0_4 + x_3_4 + x_5_4 + x_6_4 + x_7_4 + x_8_4 + x_9_4 = 1\n",
      " c14: x_0_5 + x_4_5 + x_6_5 + x_7_5 + x_8_5 + x_9_5 = 1\n",
      " c15: x_0_6 + x_3_6 + x_4_6 + x_5_6 + x_7_6 + x_8_6 + x_9_6 = 1\n",
      " c16: x_0_7 + x_4_7 + x_5_7 + x_6_7 + x_8_7 + x_9_7 + x_10_7 = 1\n",
      " c17: x_0_8 + x_3_8 + x_4_8 + x_5_8 + x_6_8 + x_7_8 + x_9_8 + x_10_8 = 1\n",
      " c18: x_0_9 + x_3_9 + x_4_9 + x_5_9 + x_6_9 + x_7_9 + x_8_9 + x_10_9 = 1\n",
      " c19: x_0_10 + x_3_10 + x_7_10 + x_8_10 + x_9_10 = 1\n",
      " c20: x_1_0 + x_2_0 + x_3_0 + x_4_0 + x_5_0 + x_6_0 + x_7_0 + x_8_0 + x_9_0\n",
      "      + x_10_0 = 3\n",
      " c21: x_0_1 + x_0_2 + x_0_3 + x_0_4 + x_0_5 + x_0_6 + x_0_7 + x_0_8 + x_0_9\n",
      "      + x_0_10 = 3\n",
      " c22: x_1_2 + x_2_1 <= 1\n",
      " c23: x_3_4 + x_4_3 <= 1\n",
      " c24: x_3_6 + x_6_3 <= 1\n",
      " c25: x_3_8 + x_8_3 <= 1\n",
      " c26: x_3_9 + x_9_3 <= 1\n",
      " c27: x_3_10 + x_10_3 <= 1\n",
      " c28: x_4_5 + x_5_4 <= 1\n",
      " c29: x_4_6 + x_6_4 <= 1\n",
      " c30: x_4_7 + x_7_4 <= 1\n",
      " c31: x_4_8 + x_8_4 <= 1\n",
      " c32: x_4_9 + x_9_4 <= 1\n",
      " c33: x_5_6 + x_6_5 <= 1\n",
      " c34: x_5_7 + x_7_5 <= 1\n",
      " c35: x_5_8 + x_8_5 <= 1\n",
      " c36: x_5_9 + x_9_5 <= 1\n",
      " c37: x_6_7 + x_7_6 <= 1\n",
      " c38: x_6_8 + x_8_6 <= 1\n",
      " c39: x_6_9 + x_9_6 <= 1\n",
      " c40: x_7_8 + x_8_7 <= 1\n",
      " c41: x_7_9 + x_9_7 <= 1\n",
      " c42: x_7_10 + x_10_7 <= 1\n",
      " c43: x_8_9 + x_9_8 <= 1\n",
      " c44: x_8_10 + x_10_8 <= 1\n",
      " c45: x_9_10 + x_10_9 <= 1\n",
      " c46: x_3_4 + x_4_3 + x_3_6 + x_6_3 + x_4_6 + x_6_4 <= 2\n",
      " c47: x_3_4 + x_4_3 + x_3_8 + x_8_3 + x_4_8 + x_8_4 <= 2\n",
      " c48: x_3_4 + x_4_3 + x_3_9 + x_9_3 + x_4_9 + x_9_4 <= 2\n",
      " c49: x_3_6 + x_6_3 + x_3_8 + x_8_3 + x_6_8 + x_8_6 <= 2\n",
      " c50: x_3_6 + x_6_3 + x_3_9 + x_9_3 + x_6_9 + x_9_6 <= 2\n",
      " c51: x_3_8 + x_8_3 + x_3_9 + x_9_3 + x_8_9 + x_9_8 <= 2\n",
      " c52: x_3_8 + x_8_3 + x_3_10 + x_10_3 + x_8_10 + x_10_8 <= 2\n",
      " c53: x_3_9 + x_9_3 + x_3_10 + x_10_3 + x_9_10 + x_10_9 <= 2\n",
      " c54: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_5_6 + x_6_5 <= 2\n",
      " c55: x_4_5 + x_5_4 + x_4_7 + x_7_4 + x_5_7 + x_7_5 <= 2\n",
      " c56: x_4_5 + x_5_4 + x_4_8 + x_8_4 + x_5_8 + x_8_5 <= 2\n",
      " c57: x_4_5 + x_5_4 + x_4_9 + x_9_4 + x_5_9 + x_9_5 <= 2\n",
      " c58: x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_6_7 + x_7_6 <= 2\n",
      " c59: x_4_6 + x_6_4 + x_4_8 + x_8_4 + x_6_8 + x_8_6 <= 2\n",
      " c60: x_4_6 + x_6_4 + x_4_9 + x_9_4 + x_6_9 + x_9_6 <= 2\n",
      " c61: x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_7_8 + x_8_7 <= 2\n",
      " c62: x_4_7 + x_7_4 + x_4_9 + x_9_4 + x_7_9 + x_9_7 <= 2\n",
      " c63: x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_8_9 + x_9_8 <= 2\n",
      " c64: x_5_6 + x_6_5 + x_5_7 + x_7_5 + x_6_7 + x_7_6 <= 2\n",
      " c65: x_5_6 + x_6_5 + x_5_8 + x_8_5 + x_6_8 + x_8_6 <= 2\n",
      " c66: x_5_6 + x_6_5 + x_5_9 + x_9_5 + x_6_9 + x_9_6 <= 2\n",
      " c67: x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_7_8 + x_8_7 <= 2\n",
      " c68: x_5_7 + x_7_5 + x_5_9 + x_9_5 + x_7_9 + x_9_7 <= 2\n",
      " c69: x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_8_9 + x_9_8 <= 2\n",
      " c70: x_6_7 + x_7_6 + x_6_8 + x_8_6 + x_7_8 + x_8_7 <= 2\n",
      " c71: x_6_7 + x_7_6 + x_6_9 + x_9_6 + x_7_9 + x_9_7 <= 2\n",
      " c72: x_6_8 + x_8_6 + x_6_9 + x_9_6 + x_8_9 + x_9_8 <= 2\n",
      " c73: x_7_8 + x_8_7 + x_7_9 + x_9_7 + x_8_9 + x_9_8 <= 2\n",
      " c74: x_7_8 + x_8_7 + x_7_10 + x_10_7 + x_8_10 + x_10_8 <= 2\n",
      " c75: x_7_9 + x_9_7 + x_7_10 + x_10_7 + x_9_10 + x_10_9 <= 2\n",
      " c76: x_8_9 + x_9_8 + x_8_10 + x_10_8 + x_9_10 + x_10_9 <= 2\n",
      " c77: x_3_4 + x_4_3 + x_3_6 + x_6_3 + x_3_8 + x_8_3 + x_4_6 + x_6_4 + x_4_8\n",
      "      + x_8_4 + x_6_8 + x_8_6 <= 3\n",
      " c78: x_3_4 + x_4_3 + x_3_6 + x_6_3 + x_3_9 + x_9_3 + x_4_6 + x_6_4 + x_4_9\n",
      "      + x_9_4 + x_6_9 + x_9_6 <= 3\n",
      " c79: x_3_4 + x_4_3 + x_3_8 + x_8_3 + x_3_9 + x_9_3 + x_4_8 + x_8_4 + x_4_9\n",
      "      + x_9_4 + x_8_9 + x_9_8 <= 3\n",
      " c80: x_3_6 + x_6_3 + x_3_8 + x_8_3 + x_3_9 + x_9_3 + x_6_8 + x_8_6 + x_6_9\n",
      "      + x_9_6 + x_8_9 + x_9_8 <= 3\n",
      " c81: x_3_8 + x_8_3 + x_3_9 + x_9_3 + x_3_10 + x_10_3 + x_8_9 + x_9_8 + x_8_10\n",
      "      + x_10_8 + x_9_10 + x_10_9 <= 3\n",
      " c82: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_5_6 + x_6_5 + x_5_7\n",
      "      + x_7_5 + x_6_7 + x_7_6 <= 3\n",
      " c83: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_8 + x_8_4 + x_5_6 + x_6_5 + x_5_8\n",
      "      + x_8_5 + x_6_8 + x_8_6 <= 3\n",
      " c84: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_9 + x_9_4 + x_5_6 + x_6_5 + x_5_9\n",
      "      + x_9_5 + x_6_9 + x_9_6 <= 3\n",
      " c85: x_4_5 + x_5_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_5_7 + x_7_5 + x_5_8\n",
      "      + x_8_5 + x_7_8 + x_8_7 <= 3\n",
      " c86: x_4_5 + x_5_4 + x_4_7 + x_7_4 + x_4_9 + x_9_4 + x_5_7 + x_7_5 + x_5_9\n",
      "      + x_9_5 + x_7_9 + x_9_7 <= 3\n",
      " c87: x_4_5 + x_5_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_5_8 + x_8_5 + x_5_9\n",
      "      + x_9_5 + x_8_9 + x_9_8 <= 3\n",
      " c88: x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_6_7 + x_7_6 + x_6_8\n",
      "      + x_8_6 + x_7_8 + x_8_7 <= 3\n",
      " c89: x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_9 + x_9_4 + x_6_7 + x_7_6 + x_6_9\n",
      "      + x_9_6 + x_7_9 + x_9_7 <= 3\n",
      " c90: x_4_6 + x_6_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_6_8 + x_8_6 + x_6_9\n",
      "      + x_9_6 + x_8_9 + x_9_8 <= 3\n",
      " c91: x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_7_8 + x_8_7 + x_7_9\n",
      "      + x_9_7 + x_8_9 + x_9_8 <= 3\n",
      " c92: x_5_6 + x_6_5 + x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_6_7 + x_7_6 + x_6_8\n",
      "      + x_8_6 + x_7_8 + x_8_7 <= 3\n",
      " c93: x_5_6 + x_6_5 + x_5_7 + x_7_5 + x_5_9 + x_9_5 + x_6_7 + x_7_6 + x_6_9\n",
      "      + x_9_6 + x_7_9 + x_9_7 <= 3\n",
      " c94: x_5_6 + x_6_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_6_8 + x_8_6 + x_6_9\n",
      "      + x_9_6 + x_8_9 + x_9_8 <= 3\n",
      " c95: x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_7_8 + x_8_7 + x_7_9\n",
      "      + x_9_7 + x_8_9 + x_9_8 <= 3\n",
      " c96: x_6_7 + x_7_6 + x_6_8 + x_8_6 + x_6_9 + x_9_6 + x_7_8 + x_8_7 + x_7_9\n",
      "      + x_9_7 + x_8_9 + x_9_8 <= 3\n",
      " c97: x_7_8 + x_8_7 + x_7_9 + x_9_7 + x_7_10 + x_10_7 + x_8_9 + x_9_8 + x_8_10\n",
      "      + x_10_8 + x_9_10 + x_10_9 <= 3\n",
      " c98: x_3_4 + x_4_3 + x_3_6 + x_6_3 + x_3_8 + x_8_3 + x_3_9 + x_9_3 + x_4_6\n",
      "      + x_6_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_6_8 + x_8_6 + x_6_9 + x_9_6\n",
      "      + x_8_9 + x_9_8 <= 4\n",
      " c99: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_5_6\n",
      "      + x_6_5 + x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_6_7 + x_7_6 + x_6_8 + x_8_6\n",
      "      + x_7_8 + x_8_7 <= 4\n",
      " c100: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_9 + x_9_4 + x_5_6\n",
      "       + x_6_5 + x_5_7 + x_7_5 + x_5_9 + x_9_5 + x_6_7 + x_7_6 + x_6_9 + x_9_6\n",
      "       + x_7_9 + x_9_7 <= 4\n",
      " c101: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_5_6\n",
      "       + x_6_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_6_8 + x_8_6 + x_6_9 + x_9_6\n",
      "       + x_8_9 + x_9_8 <= 4\n",
      " c102: x_4_5 + x_5_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_5_7\n",
      "       + x_7_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_7_8 + x_8_7 + x_7_9 + x_9_7\n",
      "       + x_8_9 + x_9_8 <= 4\n",
      " c103: x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_4_9 + x_9_4 + x_6_7\n",
      "       + x_7_6 + x_6_8 + x_8_6 + x_6_9 + x_9_6 + x_7_8 + x_8_7 + x_7_9 + x_9_7\n",
      "       + x_8_9 + x_9_8 <= 4\n",
      " c104: x_5_6 + x_6_5 + x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5 + x_6_7\n",
      "       + x_7_6 + x_6_8 + x_8_6 + x_6_9 + x_9_6 + x_7_8 + x_8_7 + x_7_9 + x_9_7\n",
      "       + x_8_9 + x_9_8 <= 4\n",
      " c105: x_4_5 + x_5_4 + x_4_6 + x_6_4 + x_4_7 + x_7_4 + x_4_8 + x_8_4 + x_4_9\n",
      "       + x_9_4 + x_5_6 + x_6_5 + x_5_7 + x_7_5 + x_5_8 + x_8_5 + x_5_9 + x_9_5\n",
      "       + x_6_7 + x_7_6 + x_6_8 + x_8_6 + x_6_9 + x_9_6 + x_7_8 + x_8_7 + x_7_9\n",
      "       + x_9_7 + x_8_9 + x_9_8 <= 5\n",
      "\n",
      "Bounds\n",
      " 0 <= x_0_1 <= 1\n",
      " 0 <= x_1_0 <= 1\n",
      " 0 <= x_0_2 <= 1\n",
      " 0 <= x_2_0 <= 1\n",
      " 0 <= x_0_3 <= 1\n",
      " 0 <= x_3_0 <= 1\n",
      " 0 <= x_0_4 <= 1\n",
      " 0 <= x_4_0 <= 1\n",
      " 0 <= x_0_5 <= 1\n",
      " 0 <= x_5_0 <= 1\n",
      " 0 <= x_0_6 <= 1\n",
      " 0 <= x_6_0 <= 1\n",
      " 0 <= x_0_7 <= 1\n",
      " 0 <= x_7_0 <= 1\n",
      " 0 <= x_0_8 <= 1\n",
      " 0 <= x_8_0 <= 1\n",
      " 0 <= x_0_9 <= 1\n",
      " 0 <= x_9_0 <= 1\n",
      " 0 <= x_0_10 <= 1\n",
      " 0 <= x_10_0 <= 1\n",
      " 0 <= x_1_2 <= 1\n",
      " 0 <= x_2_1 <= 1\n",
      " 0 <= x_3_4 <= 1\n",
      " 0 <= x_4_3 <= 1\n",
      " 0 <= x_3_6 <= 1\n",
      " 0 <= x_6_3 <= 1\n",
      " 0 <= x_3_8 <= 1\n",
      " 0 <= x_8_3 <= 1\n",
      " 0 <= x_3_9 <= 1\n",
      " 0 <= x_9_3 <= 1\n",
      " 0 <= x_3_10 <= 1\n",
      " 0 <= x_10_3 <= 1\n",
      " 0 <= x_4_5 <= 1\n",
      " 0 <= x_5_4 <= 1\n",
      " 0 <= x_4_6 <= 1\n",
      " 0 <= x_6_4 <= 1\n",
      " 0 <= x_4_7 <= 1\n",
      " 0 <= x_7_4 <= 1\n",
      " 0 <= x_4_8 <= 1\n",
      " 0 <= x_8_4 <= 1\n",
      " 0 <= x_4_9 <= 1\n",
      " 0 <= x_9_4 <= 1\n",
      " 0 <= x_5_6 <= 1\n",
      " 0 <= x_6_5 <= 1\n",
      " 0 <= x_5_7 <= 1\n",
      " 0 <= x_7_5 <= 1\n",
      " 0 <= x_5_8 <= 1\n",
      " 0 <= x_8_5 <= 1\n",
      " 0 <= x_5_9 <= 1\n",
      " 0 <= x_9_5 <= 1\n",
      " 0 <= x_6_7 <= 1\n",
      " 0 <= x_7_6 <= 1\n",
      " 0 <= x_6_8 <= 1\n",
      " 0 <= x_8_6 <= 1\n",
      " 0 <= x_6_9 <= 1\n",
      " 0 <= x_9_6 <= 1\n",
      " 0 <= x_7_8 <= 1\n",
      " 0 <= x_8_7 <= 1\n",
      " 0 <= x_7_9 <= 1\n",
      " 0 <= x_9_7 <= 1\n",
      " 0 <= x_7_10 <= 1\n",
      " 0 <= x_10_7 <= 1\n",
      " 0 <= x_8_9 <= 1\n",
      " 0 <= x_9_8 <= 1\n",
      " 0 <= x_8_10 <= 1\n",
      " 0 <= x_10_8 <= 1\n",
      " 0 <= x_9_10 <= 1\n",
      " 0 <= x_10_9 <= 1\n",
      "\n",
      "Binaries\n",
      " x_0_1 x_1_0 x_0_2 x_2_0 x_0_3 x_3_0 x_0_4 x_4_0 x_0_5 x_5_0 x_0_6 x_6_0 x_0_7\n",
      " x_7_0 x_0_8 x_8_0 x_0_9 x_9_0 x_0_10 x_10_0 x_1_2 x_2_1 x_3_4 x_4_3 x_3_6 x_6_3\n",
      " x_3_8 x_8_3 x_3_9 x_9_3 x_3_10 x_10_3 x_4_5 x_5_4 x_4_6 x_6_4 x_4_7 x_7_4 x_4_8\n",
      " x_8_4 x_4_9 x_9_4 x_5_6 x_6_5 x_5_7 x_7_5 x_5_8 x_8_5 x_5_9 x_9_5 x_6_7 x_7_6\n",
      " x_6_8 x_8_6 x_6_9 x_9_6 x_7_8 x_8_7 x_7_9 x_9_7 x_7_10 x_10_7 x_8_9 x_9_8\n",
      " x_8_10 x_10_8 x_9_10 x_10_9\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mdl = Model(name=\"VRP\")\n",
    "n_trucks = 3 # number of K trucks\n",
    "x = {}\n",
    "for i, j in G.edges():\n",
    "    x[(i, j)] = mdl.binary_var(name=f\"x_{i}_{j}\") # Adding route from company i to company j as a binary variable\n",
    "    x[(j, i)] = mdl.binary_var(name=f\"x_{j}_{i}\") # Adding route from company j to company i as a binary variable\n",
    "\n",
    "print(f\"The number of qubits needed to solve the problem is: {mdl.number_of_binary_variables}\")\n",
    "cost_func = mdl.sum(w[\"weight\"] * x[(i, j)] for i, j, w in G.edges(data=True)) + mdl.sum(w[\"weight\"] * x[(j, i)] for i, j, w in G.edges(data=True))\n",
    "mdl.minimize(cost_func)\n",
    "\n",
    "# Constraint 1a(yellow Fig. above): Only one truck goes out from company i \n",
    "for i in range(1, n_companies):\n",
    "    mdl.add_constraint(mdl.sum(x[i, j] for j in range(n_companies) if (i, j) in x.keys()) == 1)\n",
    "# Constraint 1b (yellow Fig. above): Only one truck comes into company j \n",
    "for j in range(1, n_companies):\n",
    "    mdl.add_constraint(mdl.sum(x[i, j] for i in range(n_companies) if (i, j) in x.keys()) == 1)\n",
    "# Constraint 2: (orange Fig. above) For the warehouse\n",
    "mdl.add_constraint(mdl.sum(x[i, 0] for i in range(1, n_companies)) == n_trucks)\n",
    "mdl.add_constraint(mdl.sum(x[0, j] for j in range(1, n_companies)) == n_trucks)\n",
    "\n",
    "# Constraint 3: (blue Fig. above) To eliminate sub-routes\n",
    "companies_list = list(range(1, n_companies))\n",
    "subroute_set = []\n",
    "for i in range(2, len(companies_list) + 1):\n",
    "    for comb in itertools.combinations(companies_list, i):\n",
    "        subroute_set.append(list(comb)) #subset points\n",
    "\n",
    "for subroute in subroute_set:\n",
    "    constraint_3 = []\n",
    "    for i, j in itertools.permutations(subroute, 2): #iterating over all the subset points\n",
    "        if (i, j) in x.keys():\n",
    "            constraint_3.append(x[(i,j)])\n",
    "        elif i == j:\n",
    "            pass\n",
    "        else:\n",
    "            constraint_3 = []\n",
    "            break\n",
    "    if len(constraint_3) != 0:\n",
    "        mdl.add_constraint(mdl.sum(constraint_3) <= len(subroute) - 1)\n",
    "\n",
    "quadratic_program = from_docplex_mp(mdl)\n",
    "print(quadratic_program.export_as_lp_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de52ed7",
   "metadata": {},
   "source": [
    "### Classical solution of the model\n",
    "\n",
    "Using docplex the python version of the well-known optimization solver **cplex**. We can obtain the result of the quadratic program developed above. The solution is:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b1ae875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "objective function value: 7.208211006395375\n",
      "variable values: x_0_1=1.0, x_1_0=0.0, x_0_2=0.0, x_2_0=1.0, x_0_3=0.0, x_3_0=0.0, x_0_4=0.0, x_4_0=0.0, x_0_5=1.0, x_5_0=0.0, x_0_6=0.0, x_6_0=0.0, x_0_7=0.0, x_7_0=1.0, x_0_8=1.0, x_8_0=1.0, x_0_9=0.0, x_9_0=0.0, x_0_10=0.0, x_10_0=0.0, x_1_2=1.0, x_2_1=0.0, x_3_4=0.0, x_4_3=1.0, x_3_6=0.0, x_6_3=0.0, x_3_8=0.0, x_8_3=0.0, x_3_9=1.0, x_9_3=0.0, x_3_10=0.0, x_10_3=0.0, x_4_5=0.0, x_5_4=0.0, x_4_6=0.0, x_6_4=1.0, x_4_7=0.0, x_7_4=0.0, x_4_8=0.0, x_8_4=0.0, x_4_9=0.0, x_9_4=0.0, x_5_6=1.0, x_6_5=0.0, x_5_7=0.0, x_7_5=0.0, x_5_8=0.0, x_8_5=0.0, x_5_9=0.0, x_9_5=0.0, x_6_7=0.0, x_7_6=0.0, x_6_8=0.0, x_8_6=0.0, x_6_9=0.0, x_9_6=0.0, x_7_8=0.0, x_8_7=0.0, x_7_9=0.0, x_9_7=0.0, x_7_10=0.0, x_10_7=1.0, x_8_9=0.0, x_9_8=0.0, x_8_10=0.0, x_10_8=0.0, x_9_10=1.0, x_10_9=0.0\n",
      "status: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "sol = CplexOptimizer().solve(quadratic_program)\n",
    "print(sol.prettyprint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86068af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution_cplex = sol.raw_results.as_name_dict()\n",
    "G_sol = nx.Graph()\n",
    "G_sol.add_nodes_from(range(n_companies))\n",
    "for i in solution_cplex:\n",
    "    nodes = i[2:].split(\"_\")\n",
    "    G_sol.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "nx.draw(G_sol, pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75dfcdd4",
   "metadata": {},
   "source": [
    "## Quantum Solution\n",
    "\n",
    "Once we have the quadratic program of our model. We can use the qiskit optimization library to translate it into its QUBO representation. In this case, we have inequality constraints that in most cases need extra variables, known as **slack variables**. For this specific case, the number of variables increases from  68 to 196 to represent the inequality constraints. \n",
    "\n",
    "A further constraint is the number of qubits we can simulate. Unless we have a supercomputer, the best option to simulate the largest qubits number is using **qasm_simulator** from IBM in **runtime mode**. The maximum number of qubits we can simulate using the qasm_simulator is 32. For the case of real devices, we have a similar problem, we can only run experiments on a 7 qubits device (ibm_oslo) and 16 qubits device (ibmq_guadalupe) to which we have access.\n",
    "\n",
    "Therefore, using the previous solution we can reduce the number of variables choosing the correct set of variables for the **slack variables** and for most of the route connections between companies ij, the variables we will leave are distributed as follows for the three cases analized: \n",
    "\n",
    "1) Using 7 variables and replace the orhers with the optimal solution. From the vairables left 3 have a optimal solution equal to 1 and 4 equal to 0 (testing local machine and ibm_oslo **runtime**).\n",
    "\n",
    "2) Using 25 variables and replace the others with the optimal solution. From the vairables left 10 have a optimal solution equal to 1 and 15 equal to 0 (testing qasm_simulator **runtime**).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8c818cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To represent the inital problem with 68 variables, the QUBO representation needs 196 variables\n"
     ]
    }
   ],
   "source": [
    "qubo = QuadraticProgramToQubo(penalty=15).convert(quadratic_program)\n",
    "num_vars = qubo.get_num_binary_vars()\n",
    "print(f\"To represent the inital problem with {mdl.number_of_binary_variables} variables, the QUBO representation needs {num_vars} variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83f7e5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_qubo = qubo.substitute_variables(sol.variables_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4c1b834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The solver needs 0.096 seconds to finish.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "sol_slack = CplexOptimizer().solve(new_qubo)\n",
    "end = time.time() - start\n",
    "print(f\"The solver needs {np.round(end, 3)} seconds to finish.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "95eaf242",
   "metadata": {},
   "outputs": [],
   "source": [
    "qubo_no_slack = qubo.substitute_variables(sol_slack.variables_dict)\n",
    "sol_no_slack = CplexOptimizer().solve(qubo_no_slack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27a49bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution_slack = sol_no_slack.raw_results.as_name_dict()\n",
    "G_sol_slack = nx.Graph()\n",
    "G_sol_slack.add_nodes_from(range(n_companies))\n",
    "for i in solution_slack:\n",
    "    nodes = i[2:].split(\"_\")\n",
    "    G_sol.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "nx.draw(G_sol, pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88a06873",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_1s = [k for k, v in sol_no_slack.variables_dict.items() if v == 1]\n",
    "index_0s = [k for k, v in sol_no_slack.variables_dict.items() if v == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b141ff",
   "metadata": {},
   "source": [
    "### Useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e311281b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Optimization_QAOA(qubo, reps=1, optimizer=COBYLA(maxiter=1000), backend=None,\n",
    "                      shots=1024, alpha=1.0, provider=None, local=False, error_mitigation=False):\n",
    "    intermediate_info = {'nfev': [],\n",
    "                         'parameters': [],\n",
    "                         'stddev': [],\n",
    "                         'mean': []\n",
    "                             }\n",
    "    \n",
    "    def callback(nfev, parameters, mean, stddev):\n",
    "#         display(f\"Evaluation: {nfev}, Energy: {mean}, Std: {stddev}\")\n",
    "        intermediate_info['nfev'].append(nfev)\n",
    "        intermediate_info['parameters'].append(parameters)\n",
    "        intermediate_info['mean'].append(mean)\n",
    "        intermediate_info['stddev'].append(stddev)\n",
    "    \n",
    "    if local:\n",
    "        qaoa_mes = QAOA(optimizer=optimizer, reps=reps, quantum_instance=AerSimulator(),\n",
    "                        callback=callback)\n",
    "    else:\n",
    "        qaoa_mes = QAOAClient(provider=provider, backend=backend, reps=reps, alpha=alpha,\n",
    "                             shots=shots, callback=callback, optimizer=optimizer,\n",
    "                             optimization_level=3,measurement_error_mitigation=error_mitigation)\n",
    "    qaoa = MinimumEigenOptimizer(qaoa_mes)\n",
    "    result = qaoa.solve(qubo)\n",
    "    return result, intermediate_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3148c273",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Optimization_VQE(qubo, ansatz, optimizer=SPSA(maxiter=50), backend=None,\n",
    "                     shots=1024, provider=None, local=False, error_mitigation=False):\n",
    "\n",
    "    intermediate_info = {'nfev': [],\n",
    "                         'parameters': [],\n",
    "                         'stddev': [],\n",
    "                         'mean': []\n",
    "                         }\n",
    "\n",
    "    def callback(nfev, parameters, mean, stddev):\n",
    "#         display(f\"Evaluation: {nfev}, Energy: {mean}, Std: {stddev}\")\n",
    "        intermediate_info['nfev'].append(nfev)\n",
    "        intermediate_info['parameters'].append(parameters)\n",
    "        intermediate_info['mean'].append(mean)\n",
    "        intermediate_info['stddev'].append(stddev)\n",
    "        \n",
    "    if local:\n",
    "        vqe_mes = VQE(ansatz=ansatz, quantum_instance=AerSimulator(),\n",
    "                        callback=callback, optimizer=optimizer)\n",
    "    else:\n",
    "        vqe_mes = VQEClient(ansatz=ansatz, provider=provider, backend=backend, shots=shots,\n",
    "                        callback=callback, optimizer=optimizer,measurement_error_mitigation=error_mitigation)\n",
    "    vqe = MinimumEigenOptimizer(vqe_mes)\n",
    "    result = vqe.solve(qubo)\n",
    "    return result, intermediate_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7ec392d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph(solution, solution_not_kept):\n",
    "    G = nx.Graph()\n",
    "    G.add_nodes_from(range(n_companies))\n",
    "    for k, v in solution.items():\n",
    "        if v == 1:\n",
    "            nodes = k[2:].split(\"_\")\n",
    "            G.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "    for k, v in solution_not_kept.items():\n",
    "        if v == 1:\n",
    "            nodes = k[2:].split(\"_\")\n",
    "            G.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "    return G"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d45eb36",
   "metadata": {},
   "source": [
    "## Solution of the 7 variables with qiskit runtime and local machine\n",
    "\n",
    "We tested different models to see the applicability of the current model with ideal solutions using two classical optimizer ([SPSA](https://en.wikipedia.org/wiki/Simultaneous_perturbation_stochastic_approximation) and [COBYLA](https://qiskit.org/documentation/stubs/qiskit.algorithms.optimizers.COBYLA.html#:~:text=COBYLA%20is%20a%20numerical%20optimization,scipy.optimize.minimize%20COBYLA.)) for two quantum algorithms ([QAOA](https://qiskit.org/textbook/ch-applications/qaoa.html) with 2 layer repetitions and [VQE](https://pennylane.ai/qml/demos/tutorial_vqe.html) with ansatz [TwoLocal](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html), number of shots=1024 for both cases). Additionally, we run our model in *ibm_lagos* using [qiskit runtime](https://quantum-computing.ibm.com/lab/docs/iql/runtime/) with a mitigated and unmitigated results with the same settings. The figure below shows the results for the different cases, we see that all the simulations reach the ideal solution shown in the graphs of the left, while the solution for the oslo and lagos simulations do not reach the optimal point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "81cc5753",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_1s = 3\n",
    "num_0s = 4\n",
    "random.seed(1)\n",
    "keep_1s = random.sample(index_1s, num_1s)\n",
    "keep_0s = random.sample(index_0s, num_0s)\n",
    "sol_qubo = sol.variables_dict\n",
    "solution_not_kept7 = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s} \n",
    "\n",
    "qubo_7vars = qubo_no_slack.substitute_variables(solution_not_kept7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "13cba50a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\ This file has been generated by DOcplex\n",
      "\\ ENCODING=ISO-8859-1\n",
      "\\Problem name: VRP\n",
      "\n",
      "Minimize\n",
      " obj: - 58.832732659039 x_2_0 - 59.526572232279 x_0_5 - 29.773713802415 x_0_6\n",
      "      - 119.218955212750 x_3_6 - 629.665666916984 x_6_4 - 329.617335307817 x_9_4\n",
      "      - 119.201513758697 x_9_7 + [ 60 x_2_0^2 + 60 x_0_5^2 + 60 x_0_5*x_0_6\n",
      "      + 60 x_0_6^2 + 60 x_0_6*x_3_6 + 270 x_3_6^2 + 240 x_3_6*x_6_4\n",
      "      + 120 x_3_6*x_9_4 + 630 x_6_4^2 + 660 x_6_4*x_9_4 + 240 x_6_4*x_9_7\n",
      "      + 630 x_9_4^2 + 540 x_9_4*x_9_7 + 570 x_9_7^2 ]/2 + 380.233182814698\n",
      "Subject To\n",
      "\n",
      "Bounds\n",
      " 0 <= x_2_0 <= 1\n",
      " 0 <= x_0_5 <= 1\n",
      " 0 <= x_0_6 <= 1\n",
      " 0 <= x_3_6 <= 1\n",
      " 0 <= x_6_4 <= 1\n",
      " 0 <= x_9_4 <= 1\n",
      " 0 <= x_9_7 <= 1\n",
      "\n",
      "Binaries\n",
      " x_2_0 x_0_5 x_0_6 x_3_6 x_6_4 x_9_4 x_9_7\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qubo_7vars.export_as_lp_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4784f131",
   "metadata": {},
   "source": [
    "### Solving using QAOA with COBYLA and SPSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1a9a9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_local_qaoa_cobyla = Optimization_QAOA(qubo_7vars, reps=2, optimizer=COBYLA(maxiter=100), local=True)\n",
    "sol7_local_qaoa_spsa = Optimization_QAOA(qubo_7vars, reps=2, optimizer=SPSA(maxiter=100), local=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7688e583",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2022-07-27 08:01:23,140: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q-community', group='cdl-hackathon', project='main')\n",
    "provider2 = IBMQ.get_provider(hub='ibm-q-research', group='guanajuato-1', project='main')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "132548e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_oslo_qaoa_cobyla = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,\n",
    "                                       provider=provider2, optimizer=COBYLA(maxiter=50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "01943cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_oslo_qaoa_spsa = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,\n",
    "                                       provider=provider2, optimizer=SPSA(maxiter=50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a1086c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_oslo_qaoa_spsa_mitig = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,\n",
    "                                       provider=provider2, optimizer=SPSA(maxiter=50), error_mitigation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f0b485",
   "metadata": {},
   "source": [
    "### Solving using VQE and COBYLA- SPSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "baedb5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ansatz = TwoLocal(qubo_7vars.get_num_vars(), rotation_blocks='ry', entanglement_blocks='cz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "178e65a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_local_vqe_spsa = Optimization_VQE(qubo_7vars, ansatz, local=True, optimizer=SPSA(maxiter=200))\n",
    "sol7_local_vqe_cobyla = Optimization_VQE(qubo_7vars, ansatz, local=True, optimizer=COBYLA(maxiter=200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5e20db6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_lagos_vqe_cobyla = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=COBYLA(maxiter=100),\n",
    "                                 backend=provider2.backend.ibm_lagos, provider=provider2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "eedc3b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7_lagos_vqe_spsa = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=SPSA(maxiter=25),\n",
    "                                 backend=provider2.backend.ibm_lagos, provider=provider2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "14c496c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/Avocados/lib/python3.8/site-packages/qiskit_optimization/algorithms/optimization_algorithm.py:138: UserWarning: The sum of probability of samples is not close to 1: f{sum_prob}\n",
      "  warn(\"The sum of probability of samples is not close to 1: f{sum_prob}\")\n"
     ]
    }
   ],
   "source": [
    "sol7_lagos_vqe_spsa2 = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=SPSA(maxiter=25),\n",
    "                                 backend=provider2.backend.ibm_lagos, provider=provider2, error_mitigation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783417a0",
   "metadata": {},
   "source": [
    "## 7Qubits results of the simulations and Real Devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d0b4173f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sol7_lagos_vqe_spsa[1][\"mean\"], linestyle=\":\", color=\"seagreen\", label=\"VQE-Lagos-SPSA\")\n",
    "plt.plot(sol7_lagos_vqe_spsa2[1][\"mean\"], linestyle=\"-.\", color=\"indigo\", label=\"VQE-Lagos-SPSA-Mitig\")\n",
    "plt.plot(sol7_lagos_vqe_cobyla[1][\"mean\"], color=\"olive\", label=\"VQE-Lagos-COBYLA-Mitig\")\n",
    "plt.grid()\n",
    "plt.savefig(\"./Images/inset.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "faaa7dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sol7_oslo_qaoa_spsa[1][\"mean\"], linestyle=\":\", color=\"seagreen\", label=\"QAOA-Oslo-SPSA\")\n",
    "plt.plot(sol7_oslo_qaoa_cobyla[1][\"mean\"], color=\"olive\", label=\"QAOA-Oslo-COBYLA\")\n",
    "plt.grid()\n",
    "plt.savefig(\"./Images/inset2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afb940a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol7 = {\"local_qaoa_cobyla\":sol7_local_qaoa_cobyla, \"local_vqe_cobyla\":sol7_local_vqe_cobyla,\n",
    "       \"local_qaoa_spsa\":sol7_local_qaoa_spsa, \"local_vqe_spsa\":sol7_local_vqe_spsa,\n",
    "       \"oslo_qaoa_cobyla\":sol7_oslo_qaoa_cobyla, \"oslo_qaoa_spsa\":sol7_oslo_qaoa_spsa,\n",
    "       \"lagos_vqe_spsa\":sol7_lagos_vqe_spsa, \"lagos_vqe_cobyla_mitig\":sol7_lagos_vqe_cobyla}\n",
    "np.save(\"./Data/sol7.npy\", sol7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "227fdf9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1296 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sol7 = [sol7_local_qaoa_cobyla, sol7_local_vqe_cobyla, sol7_local_qaoa_spsa, sol7_local_vqe_spsa]\n",
    "q_alg = [\"QAOA\", \"VQE\"]\n",
    "fig, ax = plt.subplots(3,2, figsize=(15,18))\n",
    "ax[0,0].plot(sol7_local_qaoa_cobyla[1][\"mean\"], color=\"darkblue\", label=\"QAOA-COBYLA\")\n",
    "ax[0,0].plot(sol7_local_qaoa_spsa[1][\"mean\"], linestyle=\"--\", color=\"lightcoral\", label=\"QAOA-SPSA\")\n",
    "ax[0,0].plot(sol7_oslo_qaoa_spsa[1][\"mean\"], linestyle=\":\", color=\"seagreen\", label=\"QAOA-Oslo-SPSA\")\n",
    "ax[0,0].plot(sol7_oslo_qaoa_cobyla[1][\"mean\"], color=\"olive\", label=\"QAOA-Oslo-COBYLA\")\n",
    "\n",
    "ax[0,1].plot(sol7_local_vqe_cobyla[1][\"mean\"], color=\"darkblue\", label=\"VQE-COBYLA\")\n",
    "ax[0,1].plot(sol7_local_vqe_spsa[1][\"mean\"], linestyle=\"--\", color=\"lightcoral\", label=\"VQE-SPSA\")\n",
    "ax[0,1].plot(sol7_lagos_vqe_spsa[1][\"mean\"], linestyle=\":\", color=\"seagreen\", label=\"VQE-Lagos-SPSA\")\n",
    "ax[0,1].plot(sol7_lagos_vqe_spsa2[1][\"mean\"], linestyle=\"-.\", color=\"indigo\", label=\"VQE-Lagos-SPSA-Mitig\")\n",
    "ax[0,1].plot(sol7_lagos_vqe_cobyla[1][\"mean\"], color=\"olive\", label=\"VQE-Lagos-COBYLA-Mitig\")\n",
    "\n",
    "for i in range(2):\n",
    "    ax[0,i].set_xlabel(\"Iterations\", fontsize=14)\n",
    "    ax[0,i].set_ylabel(\"Cost\", fontsize=14)\n",
    "    ax[0,i].legend()\n",
    "    ax[0,i].grid()\n",
    "    plt.sca(ax[1,i])\n",
    "    nx.draw(graph(sol7[i][0].variables_dict,solution_not_kept7), pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])\n",
    "    ax[1,i].set_title(f\"{q_alg[i]}-COBYLA\", fontsize=14)\n",
    "    plt.sca(ax[2,i])\n",
    "    nx.draw(graph(sol7[i+2][0].variables_dict,solution_not_kept7), pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])\n",
    "    ax[2,i].set_title(f\"{q_alg[i]}-SPSA\", fontsize=14)\n",
    "    \n",
    "plt.savefig(\"./Images/Sol7Q.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b709da9d",
   "metadata": {},
   "source": [
    "## Solution of the 15 variables with qiskit runtime on qasm_simulator\n",
    "\n",
    "For the case of 15 qubits, the solution includes only QAOA (with 3 layers repetitions) and VQE (with [TwoLocal ansatz](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html)) with the classic optimizer COBYLA. The results were obtained from qiskit runtime and the ideal simulator ibmq_qasm_simulator. As it is shown in the figure below the QAOA algorithm do not reach the solution while VQE can reach it. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f0a77974",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_1s = 5\n",
    "num_0s = 10\n",
    "random.seed(1)\n",
    "keep_1s = random.sample(index_1s, num_1s)\n",
    "keep_0s = random.sample(index_0s, num_0s)\n",
    "sol_qubo = sol.variables_dict\n",
    "solution_not_kept = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s} \n",
    "\n",
    "qubo_15vars = qubo_no_slack.substitute_variables(solution_not_kept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6056bb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol15_qasm_qaoa_cobyla = Optimization_QAOA(qubo_15vars, reps=3, local=False, backend=provider2.backend.ibmq_qasm_simulator,\n",
    "                                       provider=provider2, optimizer=COBYLA(maxiter=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "23384d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution15_qaoa =  sol15_qasm_qaoa_cobyla[0].variables_dict\n",
    "G_sol15_qaoa = nx.Graph()\n",
    "G_sol15_qaoa.add_nodes_from(range(n_companies))\n",
    "for k, v in solution15_qaoa.items():\n",
    "    if v == 1:\n",
    "        nodes = k[2:].split(\"_\")\n",
    "        G_sol15_qaoa.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "for k, v in solution_not_kept.items():\n",
    "    if v == 1:\n",
    "        nodes = k[2:].split(\"_\")\n",
    "        G_sol15_qaoa.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "nx.draw(G_sol15_qaoa, pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67b0b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol15_qasm_qaoa_spsa = Optimization_QAOA(qubo_15vars, reps=3, local=False, backend=provider2.backend.ibmq_qasm_simulator,\n",
    "                                       provider=provider2, optimizer=SPSA(maxiter=25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be240b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Evaluation: 1, Energy: -580.4372238659056, Std: 21.248365972542896'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 2, Energy: -570.4474989346885, Std: 22.567442443917827'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 3, Energy: -629.445819411282, Std: 20.198299006491634'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 4, Energy: -644.0342711305796, Std: 18.62195623091544'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 5, Energy: -559.9608195382825, Std: 21.015037406744145'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 6, Energy: -601.8525692437544, Std: 21.216848145426756'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 7, Energy: -583.8760375455167, Std: 21.708906395147256'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 8, Energy: -550.9704819443926, Std: 24.77726613021149'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 9, Energy: -613.2152546558499, Std: 22.40855702601374'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 10, Energy: -498.0799342514472, Std: 23.87542068731265'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 11, Energy: -561.8193217762555, Std: 21.479325502166354'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 12, Energy: -449.3050973753332, Std: 25.43904280649126'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 13, Energy: -577.3645927874339, Std: 20.827044648075198'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 14, Energy: -577.456804758038, Std: 22.414176614137162'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 15, Energy: -524.504643654027, Std: 23.2543429390218'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 16, Energy: -584.8529287257521, Std: 20.608672645600787'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 17, Energy: -611.0422325643189, Std: 21.014342026820596'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 18, Energy: -589.2245068446481, Std: 22.261314491782443'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 19, Energy: -607.865351326638, Std: 20.061879380976055'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 20, Energy: -589.9559869663308, Std: 20.401088172878993'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 21, Energy: -554.1500778207792, Std: 22.544478665192955'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 22, Energy: -565.0040747531388, Std: 20.229848579742864'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 23, Energy: -535.5700849669718, Std: 23.14144890916821'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 24, Energy: -568.6731825957365, Std: 21.521953264924445'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 25, Energy: -546.8582010572535, Std: 21.73221223792924'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 26, Energy: -485.63313492564447, Std: 23.61297093734926'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 27, Energy: -508.40401276404015, Std: 22.965189992624996'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 28, Energy: -672.5015737393372, Std: 21.933109478200986'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 29, Energy: -607.3896096849571, Std: 27.07932037331175'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 30, Energy: -582.2304218502167, Std: 24.21275223491138'"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 31, Energy: -696.0709879783643, Std: 22.616020736392766'"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 32, Energy: -695.0200910325115, Std: 22.530743287774616'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 33, Energy: -661.7480747726969, Std: 21.898347863345563'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 34, Energy: -724.3905680356443, Std: 22.01981472959951'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 35, Energy: -664.8409757745736, Std: 22.75809986709807'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 36, Energy: -638.053210956126, Std: 23.36397871850549'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 37, Energy: -622.375875030731, Std: 25.074011711001372'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 38, Energy: -545.5333152468666, Std: 25.818674349612223'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 39, Energy: -691.9991098867285, Std: 23.070517551895108'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 40, Energy: -557.0029039091391, Std: 25.598595877285288'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 41, Energy: -602.5058616985317, Std: 24.51977323802882'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 42, Energy: -515.8069208724316, Std: 27.347802591857576'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 43, Energy: -644.4201120685387, Std: 21.916402338444176'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 44, Energy: -605.1589770185915, Std: 24.35072833155374'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 45, Energy: -558.9632477396307, Std: 24.63601669902785'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 46, Energy: -660.8080998633912, Std: 22.716426615546315'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 47, Energy: -697.3319238816176, Std: 21.877857008726224'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 48, Energy: -633.901529378183, Std: 23.584365608862036'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 49, Energy: -698.1243528763184, Std: 22.46723681493793'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 50, Energy: -665.5459693507828, Std: 21.701893143172295'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 51, Energy: -646.5513160070929, Std: 23.662794944978412'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 52, Energy: -676.0590999663407, Std: 22.404952133818064'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 53, Energy: -641.1758588535123, Std: 22.36601166899659'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 54, Energy: -667.389389269687, Std: 24.535091660043125'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 55, Energy: -615.2027076562787, Std: 23.17169022998951'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 56, Energy: -570.3650334265899, Std: 22.61613319088157'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 57, Energy: -584.2251535371134, Std: 23.1082257298509'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 58, Energy: -590.5546045015793, Std: 23.517770286221985'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 59, Energy: -594.6799569487896, Std: 24.312702781574437'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 60, Energy: -622.6988272434783, Std: 22.387363315549777'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 61, Energy: -672.4038206926742, Std: 21.949781134495822'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 62, Energy: -936.6718953562757, Std: 12.332222043339705'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 63, Energy: -883.0057133212217, Std: 13.916878561470847'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 64, Energy: -956.8383482797595, Std: 11.969976535777935'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 65, Energy: -946.5549029223615, Std: 12.368656094209243'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 66, Energy: -947.7606490910514, Std: 12.75631789268945'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 67, Energy: -892.1039146507121, Std: 14.087447705103743'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 68, Energy: -967.6012990065369, Std: 11.774367547370963'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 69, Energy: -945.5893188978511, Std: 13.062749727352493'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 70, Energy: -955.6494832648829, Std: 12.460607461555206'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 71, Energy: -974.0348542345727, Std: 12.105674100386462'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 72, Energy: -942.3059052122653, Std: 13.47722956222804'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 73, Energy: -954.7714980846362, Std: 12.086437929924967'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 74, Energy: -946.6690721434156, Std: 12.987797021108936'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 75, Energy: -914.6680054752067, Std: 13.613353655168487'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 76, Energy: -954.227706544618, Std: 12.741320493602561'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 77, Energy: -982.683394276828, Std: 12.086341950215248'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 78, Energy: -982.5209569633362, Std: 11.750597116895388'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 79, Energy: -963.1282158921664, Std: 11.44041099381524'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 80, Energy: -981.6522590336909, Std: 11.775642591936494'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 81, Energy: -1008.4471966105398, Std: 10.918873318600648'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 82, Energy: -1005.7369761580894, Std: 11.189962616835816'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 83, Energy: -994.684635645674, Std: 10.494398442550358'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 84, Energy: -991.5482986953139, Std: 11.273411207778322'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 85, Energy: -1002.2692820038795, Std: 10.283347704328078'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 86, Energy: -997.7360380296738, Std: 10.278735656308253'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 87, Energy: -1031.971686922621, Std: 11.495806590919296'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 88, Energy: -1019.6444747874004, Std: 12.018075936164985'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 89, Energy: -893.5985894883952, Std: 12.367676248369023'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 90, Energy: -1066.6943874456338, Std: 11.320145017683116'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 91, Energy: -947.4491495775563, Std: 11.892992071008253'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 92, Energy: -1030.3888773974609, Std: 12.183036909853985'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 93, Energy: -1074.0500617765329, Std: 11.009495097288077'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 94, Energy: -1089.0235575875413, Std: 11.328801574796973'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 95, Energy: -1077.148611936441, Std: 12.058863617042038'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 96, Energy: -1060.692668879035, Std: 11.029480795384462'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 97, Energy: -1102.126096750582, Std: 10.511221134731787'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 98, Energy: -1114.5276526763375, Std: 10.694530099775605'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 99, Energy: -1044.8985731835971, Std: 10.949638054795871'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 100, Energy: -1101.3781527390524, Std: 10.410431966103772'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 101, Energy: -1149.5814231166767, Std: 9.153215253002031'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 102, Energy: -1133.0015357065192, Std: 9.383433518967882'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 103, Energy: -1158.9779523187253, Std: 9.378395513335512'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 104, Energy: -1139.0360789821414, Std: 9.438291254703394'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 105, Energy: -1149.504837515996, Std: 8.742742007573208'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 106, Energy: -1136.9005254690849, Std: 9.006315661083427'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 107, Energy: -1175.3124999070906, Std: 8.749660466994351'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 108, Energy: -1171.589158891284, Std: 8.672586363010828'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 109, Energy: -1182.858715536197, Std: 9.448799691080296'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 110, Energy: -1173.0107697987762, Std: 9.05190331455625'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 111, Energy: -1176.0619936716175, Std: 8.786236630667913'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 112, Energy: -1191.592267350223, Std: 8.37965917508967'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 113, Energy: -1191.058453529149, Std: 9.424628630107593'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 114, Energy: -1186.1549873519082, Std: 8.583544435250314'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 115, Energy: -1187.5775273919558, Std: 8.675417046424293'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 116, Energy: -1191.4082543244972, Std: 8.735858172934696'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 117, Energy: -1180.35583982139, Std: 9.015455627898781'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 118, Energy: -1189.7640696409696, Std: 8.799242114031609'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 119, Energy: -1198.800247740049, Std: 8.853705629991415'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 120, Energy: -1198.1491449950959, Std: 8.848111079049282'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 121, Energy: -1178.299518577296, Std: 8.883222606872739'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 122, Energy: -1199.6544596024437, Std: 8.602272494888878'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 123, Energy: -1090.2732399247316, Std: 10.903965317090767'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 124, Energy: -1197.880592861993, Std: 8.939554025523632'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 125, Energy: -1173.07268611098, Std: 9.590633673268368'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
       "'Evaluation: 126, Energy: -1177.638391459668, Std: 9.382745754463704'"
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     "data": {
      "text/plain": [
       "'Evaluation: 127, Energy: -1123.872358797389, Std: 11.999237122403287'"
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     "data": {
      "text/plain": [
       "'Evaluation: 128, Energy: -1202.9333051869369, Std: 9.361689969466921'"
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     "data": {
      "text/plain": [
       "'Evaluation: 129, Energy: -1229.8309299300633, Std: 9.297145934089878'"
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     "data": {
      "text/plain": [
       "'Evaluation: 130, Energy: -1186.7336345289186, Std: 9.640316993296125'"
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     "data": {
      "text/plain": [
       "'Evaluation: 131, Energy: -1139.397863001602, Std: 12.287074030676399'"
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     "data": {
      "text/plain": [
       "'Evaluation: 132, Energy: -1252.9590623505587, Std: 8.42886320091212'"
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     "data": {
      "text/plain": [
       "'Evaluation: 133, Energy: -1198.1890093059903, Std: 10.461622890688895'"
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     "data": {
      "text/plain": [
       "'Evaluation: 134, Energy: -1177.7700004858443, Std: 10.435820680398432'"
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     "data": {
      "text/plain": [
       "'Evaluation: 135, Energy: -1226.640484133494, Std: 8.283352518351835'"
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      "text/plain": [
       "'Evaluation: 136, Energy: -1241.066318496021, Std: 9.183332034690004'"
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     "data": {
      "text/plain": [
       "'Evaluation: 137, Energy: -1247.237244423066, Std: 9.114037488383104'"
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     "data": {
      "text/plain": [
       "'Evaluation: 138, Energy: -1242.3653083819872, Std: 8.716002425242559'"
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     "data": {
      "text/plain": [
       "'Evaluation: 139, Energy: -1214.7626016256181, Std: 9.321039560537454'"
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     "data": {
      "text/plain": [
       "'Evaluation: 140, Energy: -1233.8723044765466, Std: 8.880741993813881'"
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     "data": {
      "text/plain": [
       "'Evaluation: 141, Energy: -1192.9367589769133, Std: 8.801982738451528'"
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     "data": {
      "text/plain": [
       "'Evaluation: 142, Energy: -1222.8376919137952, Std: 9.106722383946996'"
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     "data": {
      "text/plain": [
       "'Evaluation: 143, Energy: -1228.4538329377121, Std: 8.274986425754129'"
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     "data": {
      "text/plain": [
       "'Evaluation: 144, Energy: -1214.3855319566883, Std: 8.904167834411869'"
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     "data": {
      "text/plain": [
       "'Evaluation: 145, Energy: -1160.368437762885, Std: 11.502920521419574'"
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     "data": {
      "text/plain": [
       "'Evaluation: 146, Energy: -1223.7706631931824, Std: 9.354109250527312'"
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     "data": {
      "text/plain": [
       "'Evaluation: 147, Energy: -1143.5154677575283, Std: 10.94963370625348'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 148, Energy: -1227.3436014074257, Std: 9.214576269650795'"
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     "data": {
      "text/plain": [
       "'Evaluation: 149, Energy: -1212.1921207422808, Std: 8.50062362371661'"
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     "data": {
      "text/plain": [
       "'Evaluation: 150, Energy: -1230.8861144616278, Std: 8.581573734160308'"
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     "data": {
      "text/plain": [
       "'Evaluation: 151, Energy: -1236.8404156888732, Std: 8.76472398791971'"
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     "data": {
      "text/plain": [
       "'Evaluation: 152, Energy: -1222.4621151153387, Std: 8.845174368324228'"
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     "data": {
      "text/plain": [
       "'Evaluation: 153, Energy: -1172.4315589295325, Std: 9.631002873281592'"
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     "data": {
      "text/plain": [
       "'Evaluation: 154, Energy: -1241.757381871124, Std: 9.12466710109234'"
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     "data": {
      "text/plain": [
       "'Evaluation: 155, Energy: -1207.977308627612, Std: 9.639178045956905'"
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     "data": {
      "text/plain": [
       "'Evaluation: 156, Energy: -1183.6164404576457, Std: 9.604287335477967'"
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     "data": {
      "text/plain": [
       "'Evaluation: 157, Energy: -1278.0321680056593, Std: 8.186075527164066'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 158, Energy: -1242.2582105092906, Std: 9.217241635602914'"
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     "data": {
      "text/plain": [
       "'Evaluation: 159, Energy: -1275.8790418688689, Std: 8.61750182342266'"
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     "data": {
      "text/plain": [
       "'Evaluation: 160, Energy: -1248.1457871772025, Std: 9.675009900682477'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 161, Energy: -1245.7508546000315, Std: 9.361347144707802'"
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     "data": {
      "text/plain": [
       "'Evaluation: 162, Energy: -1250.933432251301, Std: 8.888526683529948'"
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     "data": {
      "text/plain": [
       "'Evaluation: 163, Energy: -1273.1199797886577, Std: 8.066819681396671'"
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     "data": {
      "text/plain": [
       "'Evaluation: 164, Energy: -1235.919389527846, Std: 8.77131585997608'"
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      "text/plain": [
       "'Evaluation: 165, Energy: -1275.2540979346575, Std: 7.954991168710328'"
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     "data": {
      "text/plain": [
       "'Evaluation: 166, Energy: -1229.9195585231096, Std: 9.16356047850107'"
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      "text/plain": [
       "'Evaluation: 167, Energy: -1251.881006441077, Std: 9.353902300020689'"
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      "text/plain": [
       "'Evaluation: 168, Energy: -1271.073128252553, Std: 8.522212943051821'"
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     "data": {
      "text/plain": [
       "'Evaluation: 169, Energy: -1270.6348930379152, Std: 8.1894049921001'"
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     "data": {
      "text/plain": [
       "'Evaluation: 170, Energy: -1229.6343401817444, Std: 8.422220863834806'"
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     "data": {
      "text/plain": [
       "'Evaluation: 171, Energy: -1251.3179642861521, Std: 8.331387090026896'"
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     "data": {
      "text/plain": [
       "'Evaluation: 172, Energy: -1246.979644806295, Std: 8.235454216038256'"
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     "data": {
      "text/plain": [
       "'Evaluation: 173, Energy: -1203.04608322529, Std: 8.328205468351097'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 174, Energy: -1254.9550283184128, Std: 8.505999893545864'"
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     "data": {
      "text/plain": [
       "'Evaluation: 175, Energy: -1243.322382582519, Std: 9.093411512031349'"
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     "data": {
      "text/plain": [
       "'Evaluation: 176, Energy: -1245.46192202243, Std: 8.463010445213026'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 177, Energy: -1285.0584708769113, Std: 9.129440396381616'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 178, Energy: -1272.2920753472856, Std: 8.121605842902504'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 179, Energy: -1227.7248673158683, Std: 9.006214884204672'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 180, Energy: -1300.7931107204056, Std: 8.207046981449247'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 181, Energy: -1250.613279566646, Std: 9.537244768411355'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 182, Energy: -1294.1324478174686, Std: 8.592208197881497'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 183, Energy: -1286.1217323151527, Std: 8.39066811740586'"
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      "text/plain": [
       "'Evaluation: 184, Energy: -1292.7899110132619, Std: 7.9596522020243325'"
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     "data": {
      "text/plain": [
       "'Evaluation: 185, Energy: -1269.1740685728935, Std: 8.890222739390634'"
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     "data": {
      "text/plain": [
       "'Evaluation: 186, Energy: -1305.1786601031092, Std: 8.08171642279196'"
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      "text/plain": [
       "'Evaluation: 187, Energy: -1262.248375800491, Std: 9.833451824090117'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 188, Energy: -1275.3204030027746, Std: 8.723743703139267'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 189, Energy: -1285.207558595678, Std: 8.424087802796885'"
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     "data": {
      "text/plain": [
       "'Evaluation: 190, Energy: -1305.9044398969088, Std: 8.169760617562716'"
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     "data": {
      "text/plain": [
       "'Evaluation: 191, Energy: -1231.9130603863305, Std: 9.276234726674558'"
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      "text/plain": [
       "'Evaluation: 192, Energy: -1286.829096900796, Std: 8.207982848211063'"
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     "data": {
      "text/plain": [
       "'Evaluation: 193, Energy: -1237.7202075751661, Std: 9.115179905939549'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 194, Energy: -1290.0355424388736, Std: 8.438727488584158'"
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     "data": {
      "text/plain": [
       "'Evaluation: 195, Energy: -1348.2926314935967, Std: 6.976527629081447'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 196, Energy: -1290.0869379506717, Std: 9.12772379396525'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 197, Energy: -1320.1463803866027, Std: 7.857957321662307'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 198, Energy: -1300.8690103548468, Std: 8.299501643468036'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 199, Energy: -1329.436190149779, Std: 7.219502373683193'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 200, Energy: -1265.623661658867, Std: 8.643142059761727'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 201, Energy: -1321.8499388624348, Std: 8.13668235975545'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 202, Energy: -1316.559550479013, Std: 7.843050900563307'"
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    {
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      "text/plain": [
       "'Evaluation: 203, Energy: -1345.9337207618219, Std: 7.056962897097264'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 204, Energy: -1307.2901083928757, Std: 7.820790485607656'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 205, Energy: -1314.1368781870708, Std: 8.56434585148'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 206, Energy: -1291.0383603953665, Std: 7.9346437572248165'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 207, Energy: -1296.910903465915, Std: 8.753788005232417'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 208, Energy: -1261.4095852942537, Std: 9.362449469306053'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 209, Energy: -1343.713884630865, Std: 7.598846998147696'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 210, Energy: -1339.5431164673807, Std: 6.949307122158935'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 211, Energy: -1328.202874753979, Std: 7.578752212665835'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 212, Energy: -1351.5917109388108, Std: 6.902973476694683'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 213, Energy: -1355.1824785601675, Std: 7.002169835937538'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 214, Energy: -1308.453898001096, Std: 7.528559612275052'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 215, Energy: -1338.9231775729368, Std: 7.787865112533832'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 216, Energy: -1283.8968538630502, Std: 7.933384382654137'"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 217, Energy: -1337.2600115656514, Std: 7.141854401395506'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 218, Energy: -1335.5958047222286, Std: 6.84228142466075'"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 219, Energy: -1315.910824271777, Std: 7.854743887801079'"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 220, Energy: -1318.6680946047081, Std: 7.953479615101229'"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 221, Energy: -1310.8228420043984, Std: 7.4971797163219644'"
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     },
     "metadata": {},
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    {
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       "'Evaluation: 222, Energy: -1326.990014652432, Std: 6.960519707255879'"
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     "data": {
      "text/plain": [
       "'Evaluation: 223, Energy: -1327.4312704760594, Std: 7.662860383772369'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 224, Energy: -1300.7798320584789, Std: 8.605282475236798'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 225, Energy: -1357.8344194109052, Std: 7.136919790280297'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 226, Energy: -1290.0227725800726, Std: 8.635946684981452'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 227, Energy: -1335.887989411155, Std: 7.5006017055374565'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 228, Energy: -1273.534056152095, Std: 8.32569064928962'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 229, Energy: -1319.8694035385952, Std: 7.705993640644868'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 230, Energy: -1324.5712635123853, Std: 7.746189305950121'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 231, Energy: -1349.0594203431333, Std: 6.762946959764924'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 232, Energy: -1291.4361181063507, Std: 8.155307841159125'"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 233, Energy: -1344.933934034819, Std: 6.99044986842346'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 234, Energy: -1334.7949513691062, Std: 6.500628560578338'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 235, Energy: -1336.1649279756236, Std: 7.634657219342432'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 236, Energy: -1302.0032487095914, Std: 8.492500800862974'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 237, Energy: -1332.0530560619159, Std: 7.5668236429126345'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 238, Energy: -1337.8883563537777, Std: 7.524429430928594'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 239, Energy: -1344.8157937288292, Std: 7.448001008341213'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 240, Energy: -1324.6427967555692, Std: 8.103616877810138'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 241, Energy: -1369.5587773621353, Std: 6.829495548539776'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 242, Energy: -1339.1704275358868, Std: 7.452534359992289'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 243, Energy: -1380.5097328241764, Std: 6.36056777408897'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 244, Energy: -1340.778657955993, Std: 8.183447594225958'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 245, Energy: -1387.9619508818196, Std: 6.009260438757449'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 246, Energy: -1363.1832678752914, Std: 7.1850058546287805'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 247, Energy: -1334.3497808297066, Std: 7.512914168407867'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 248, Energy: -1364.2903165347177, Std: 7.270404118352937'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 249, Energy: -1327.7099097365247, Std: 7.4585666078951265'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 250, Energy: -1356.5842913872755, Std: 7.190695527292682'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 251, Energy: -1383.0953777068362, Std: 6.477978262892834'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 252, Energy: -1326.0460583344384, Std: 7.443650524783981'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 253, Energy: -1361.207677952327, Std: 6.868094448905802'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 254, Energy: -1388.2473555312517, Std: 6.213081660770154'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 255, Energy: -1379.4510369584268, Std: 6.5603384885123175'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 256, Energy: -1378.470111743, Std: 6.2053327603514035'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 257, Energy: -1387.1722105689796, Std: 5.885299360081433'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 258, Energy: -1364.9134956161477, Std: 6.526913222858934'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 259, Energy: -1380.3643345940689, Std: 6.201038941736346'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 260, Energy: -1367.8320900622716, Std: 5.887433186503957'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 261, Energy: -1379.3912364984053, Std: 6.466613562966406'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 262, Energy: -1311.0103493393356, Std: 7.333747037659209'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 263, Energy: -1380.1691907778188, Std: 6.222284880218866'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 264, Energy: -1372.1101883690292, Std: 6.535018716246905'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 265, Energy: -1374.3182665860445, Std: 6.22830555764285'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 266, Energy: -1368.24066729591, Std: 6.746140223564133'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 267, Energy: -1364.971292163702, Std: 6.287862247700003'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 268, Energy: -1391.1067870388522, Std: 6.221092047032331'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 269, Energy: -1396.0820287908903, Std: 6.329701741986531'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 270, Energy: -1332.3190596647619, Std: 6.9747787900130485'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 271, Energy: -1377.689402199217, Std: 6.538044754512459'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 272, Energy: -1342.9395896156602, Std: 7.343837751011711'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 273, Energy: -1380.5949804304473, Std: 6.475425588790224'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 274, Energy: -1359.5698774174202, Std: 6.732943113377874'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 275, Energy: -1388.096481239585, Std: 6.370736516756352'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 276, Energy: -1390.1787657658151, Std: 5.664857605865039'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 277, Energy: -1393.1820597479452, Std: 6.571268144398921'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 278, Energy: -1363.2357405417529, Std: 6.399885092749792'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 279, Energy: -1374.8898146617098, Std: 6.462203167002275'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 280, Energy: -1380.999897233908, Std: 6.2384047220291725'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 281, Energy: -1388.28642145866, Std: 6.44876504889566'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 282, Energy: -1378.9119045545885, Std: 6.437156885766353'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 283, Energy: -1392.366413301174, Std: 6.39970834670859'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 284, Energy: -1357.80406145158, Std: 6.904685640045208'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 285, Energy: -1356.6098302851829, Std: 7.069359896032535'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 286, Energy: -1382.0150394784685, Std: 6.581816359046358'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 287, Energy: -1385.0739146185585, Std: 6.452439663438839'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 288, Energy: -1393.412843922415, Std: 6.446977542236099'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 289, Energy: -1392.9231111386673, Std: 6.338587763921178'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 290, Energy: -1396.2341509251348, Std: 6.078341267062305'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 291, Energy: -1397.8346677718305, Std: 6.221449734749306'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 292, Energy: -1397.5966841785698, Std: 6.171902305113916'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 293, Energy: -1386.5982068515796, Std: 6.123223772373725'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 294, Energy: -1391.5062711534702, Std: 6.379659481083303'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 295, Energy: -1381.7059926369425, Std: 6.723288290078589'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 296, Energy: -1392.9413740203695, Std: 6.277397409565646'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 297, Energy: -1399.530138152163, Std: 6.256699755174361'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 298, Energy: -1386.6911104994645, Std: 6.322910815475688'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 299, Energy: -1376.969288571467, Std: 6.783476256235949'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 300, Energy: -1389.6494654147107, Std: 6.2816991115698455'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 301, Energy: -1396.6223805585842, Std: 6.815006249055028'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 302, Energy: -1385.7786897949682, Std: 6.230460488812571'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 303, Energy: -1378.0983077071412, Std: 6.187350186201802'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 304, Energy: -1380.9706368130173, Std: 6.5007356362033315'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 305, Energy: -1378.4389634635813, Std: 6.889467070399308'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 306, Energy: -1389.1330338019384, Std: 6.171647496120131'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 307, Energy: -1370.5474889285006, Std: 6.833588665910511'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 308, Energy: -1403.3346984814586, Std: 6.185882191448261'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 309, Energy: -1388.6688816859328, Std: 6.2550895825060095'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 310, Energy: -1395.9979093233499, Std: 6.280638793993571'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 311, Energy: -1367.7790264413215, Std: 6.764508188266977'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 312, Energy: -1376.525744021341, Std: 6.434496344260259'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 313, Energy: -1375.8431203692508, Std: 6.500124082392272'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 314, Energy: -1395.890007184553, Std: 6.1454438447407815'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 315, Energy: -1391.5014756824125, Std: 6.454444469574596'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 316, Energy: -1406.8690670897977, Std: 6.073392972554679'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 317, Energy: -1382.350599545688, Std: 6.459281345647728'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 318, Energy: -1401.34150946239, Std: 6.253564486932981'"
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     },
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     "data": {
      "text/plain": [
       "'Evaluation: 319, Energy: -1395.7848848845251, Std: 6.05516919500633'"
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     },
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     "data": {
      "text/plain": [
       "'Evaluation: 320, Energy: -1390.251186305834, Std: 6.690712128495545'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 321, Energy: -1395.4145048855175, Std: 6.164261353648132'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 322, Energy: -1392.1445386371342, Std: 6.1623077158880974'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 323, Energy: -1417.592939181451, Std: 5.930929076436023'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 324, Energy: -1405.3467784619074, Std: 6.112866421001846'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 325, Energy: -1392.942447956807, Std: 6.455559496360594'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 326, Energy: -1404.6330822080017, Std: 5.9274304506294095'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 327, Energy: -1416.8558887981585, Std: 6.229239618744513'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 328, Energy: -1397.7602162685166, Std: 6.247930395043276'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 329, Energy: -1404.6774079030233, Std: 6.501127764607547'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 330, Energy: -1401.455420243402, Std: 6.107838892529838'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 331, Energy: -1389.9320223787051, Std: 6.232410838231346'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 332, Energy: -1397.5519739864365, Std: 6.3033406355575465'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 333, Energy: -1404.5879692464782, Std: 6.188958589568982'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 334, Energy: -1395.805210323521, Std: 6.3970110646896705'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 335, Energy: -1401.173184267745, Std: 5.99345513042953'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 336, Energy: -1400.632500200072, Std: 6.162103429402837'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 337, Energy: -1413.6253018594264, Std: 6.178764935142188'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 338, Energy: -1397.7303606185837, Std: 6.155672155832288'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 339, Energy: -1394.2487093914153, Std: 6.411212924961378'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 340, Energy: -1397.461797285005, Std: 6.314781300883468'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 341, Energy: -1391.0532404228845, Std: 6.1461206731052656'"
      ]
     },
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     "data": {
      "text/plain": [
       "'Evaluation: 342, Energy: -1404.975824647479, Std: 6.248152820321583'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 343, Energy: -1420.5360232458424, Std: 6.058540250174387'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 344, Energy: -1409.1590129025985, Std: 6.127460453866754'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 345, Energy: -1405.518751634441, Std: 6.766481745487215'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 346, Energy: -1414.183058537714, Std: 6.036980437532562'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 347, Energy: -1418.015785842313, Std: 6.121582683298008'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 348, Energy: -1416.9187219771147, Std: 6.187823683804086'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 349, Energy: -1411.6199224371169, Std: 6.000063652935762'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 350, Energy: -1397.8143436534501, Std: 5.915101553533224'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 351, Energy: -1398.401232619842, Std: 6.302983175755561'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 352, Energy: -1405.1486026965677, Std: 6.522493317687357'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 353, Energy: -1400.7044073193263, Std: 6.401737638742887'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 354, Energy: -1410.769468813874, Std: 6.263712576453529'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 355, Energy: -1413.661839166082, Std: 5.98849131606404'"
      ]
     },
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     "data": {
      "text/plain": [
       "'Evaluation: 356, Energy: -1402.8520552220762, Std: 6.406701373804614'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 357, Energy: -1396.954470332769, Std: 6.222767354093287'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 358, Energy: -1406.1825075262127, Std: 6.2929165710532'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 359, Energy: -1441.4496677575132, Std: 5.691167588136377'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 360, Energy: -1424.146793442626, Std: 5.830723693226953'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 361, Energy: -1434.1371337944022, Std: 5.7061941285209175'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 362, Energy: -1445.8261796433892, Std: 5.49302673529772'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 363, Energy: -1422.2383742284833, Std: 6.041741971647654'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 364, Energy: -1436.8222486711602, Std: 5.820901775105101'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 365, Energy: -1429.432684219042, Std: 6.143109972682507'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 366, Energy: -1420.7698410284263, Std: 6.380991116408767'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 367, Energy: -1429.385748837603, Std: 5.95657184336796'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 368, Energy: -1433.913312249513, Std: 5.41414832732399'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 369, Energy: -1415.7264866148867, Std: 6.267639381630002'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 370, Energy: -1426.1971997806297, Std: 6.613460080907049'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 371, Energy: -1426.0693365958898, Std: 5.94831293239982'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 372, Energy: -1438.3574949872602, Std: 5.782855095137721'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 373, Energy: -1441.1323360269669, Std: 5.823842812457168'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 374, Energy: -1429.339413807775, Std: 5.9272852993006815'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 375, Energy: -1434.4178123291297, Std: 5.864177777365458'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 376, Energy: -1430.0534955223727, Std: 5.890598887261125'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 377, Energy: -1441.9025480230962, Std: 5.821172255191513'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 378, Energy: -1431.8950903517025, Std: 6.399104836396859'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 379, Energy: -1438.5336367811026, Std: 5.7156314374921555'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 380, Energy: -1432.3402787814077, Std: 5.861328831984054'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 381, Energy: -1441.6123530361394, Std: 5.602512872050975'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 382, Energy: -1439.3232366616699, Std: 5.80737222535949'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 383, Energy: -1430.5053546526524, Std: 5.890931082446069'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 384, Energy: -1438.323332958337, Std: 5.645452839435173'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 385, Energy: -1445.1498095234663, Std: 5.880058913488828'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 386, Energy: -1436.8416831413306, Std: 5.769331065174509'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 387, Energy: -1444.019191493087, Std: 5.477060297563073'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 388, Energy: -1444.2975017155625, Std: 5.749367680751606'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 389, Energy: -1440.4844926445762, Std: 5.8034491740199465'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 390, Energy: -1453.4999044490653, Std: 5.617198185322735'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 391, Energy: -1425.6122374032275, Std: 6.254795867853295'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 392, Energy: -1441.1033014044901, Std: 5.86426820989803'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 393, Energy: -1436.0336193750531, Std: 5.976877443198911'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 394, Energy: -1438.190123996709, Std: 5.631956173517984'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 395, Energy: -1448.3613531930278, Std: 5.495681172906961'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 396, Energy: -1444.8534128923222, Std: 5.834971805799602'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 397, Energy: -1446.7508025762145, Std: 5.395799882345353'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 398, Energy: -1449.8953284186111, Std: 5.398487626257783'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 399, Energy: -1447.9332275639777, Std: 5.721762776758752'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 400, Energy: -1439.9510958535593, Std: 5.650007543993332'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 401, Energy: -1447.6547469221969, Std: 5.698258426171581'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 402, Energy: -1444.874429428621, Std: 5.7470413978527475'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 403, Energy: -1440.3487108107238, Std: 5.563153285488146'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 404, Energy: -1459.660524853649, Std: 5.299290955697882'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 405, Energy: -1439.8864820323583, Std: 5.7765944434967205'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 406, Energy: -1443.7249800261914, Std: 5.746716524983264'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 407, Energy: -1441.7837799829795, Std: 5.742386487212428'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 408, Energy: -1448.4951522333486, Std: 5.429587522184912'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 409, Energy: -1444.3772413953627, Std: 5.907720486109979'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 410, Energy: -1443.4430286285706, Std: 5.729050129923085'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 411, Energy: -1453.7877040897597, Std: 5.544419607310782'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 412, Energy: -1445.7315945511841, Std: 5.6287458970655075'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 413, Energy: -1428.6572354783536, Std: 5.779577346225452'"
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     },
     "metadata": {},
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    {
     "data": {
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       "'Evaluation: 414, Energy: -1447.5702972268732, Std: 5.538996768422587'"
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     "data": {
      "text/plain": [
       "'Evaluation: 415, Energy: -1441.3867358025648, Std: 5.822007487731901'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 416, Energy: -1437.091765671399, Std: 6.021305126933797'"
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     "data": {
      "text/plain": [
       "'Evaluation: 417, Energy: -1443.2063179802449, Std: 5.983409485989615'"
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     "data": {
      "text/plain": [
       "'Evaluation: 418, Energy: -1437.953154627771, Std: 5.683923282924824'"
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     "data": {
      "text/plain": [
       "'Evaluation: 419, Energy: -1437.7017562307672, Std: 6.563250635091991'"
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     "data": {
      "text/plain": [
       "'Evaluation: 420, Energy: -1436.159936659749, Std: 5.836566050343311'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 421, Energy: -1451.2816239515957, Std: 5.448909588488324'"
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     "data": {
      "text/plain": [
       "'Evaluation: 422, Energy: -1456.2554459354756, Std: 5.438432625371788'"
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     "data": {
      "text/plain": [
       "'Evaluation: 423, Energy: -1437.468898596785, Std: 5.806020671368672'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 424, Energy: -1451.5396998172203, Std: 5.455595723723914'"
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     "data": {
      "text/plain": [
       "'Evaluation: 425, Energy: -1436.2415188489088, Std: 5.742473748300356'"
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     "data": {
      "text/plain": [
       "'Evaluation: 426, Energy: -1452.6478770794204, Std: 5.330795644496279'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 427, Energy: -1436.092353277959, Std: 5.628271486823246'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 428, Energy: -1458.4211103864236, Std: 5.311881248803715'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 429, Energy: -1445.597629941764, Std: 5.760714571263233'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 430, Energy: -1437.2098356528454, Std: 5.988367969296116'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 431, Energy: -1447.7234426978425, Std: 5.758611024925229'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 432, Energy: -1440.190643069478, Std: 5.821108733407272'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 433, Energy: -1446.4580545716424, Std: 5.55088331887829'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 434, Energy: -1442.749791587285, Std: 5.88045619294763'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 435, Energy: -1443.8844426576836, Std: 5.755057062181465'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 436, Energy: -1441.7653631895714, Std: 5.886661036429024'"
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     "data": {
      "text/plain": [
       "'Evaluation: 437, Energy: -1436.2358940110962, Std: 6.0182823253788476'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 438, Energy: -1442.4605411559016, Std: 5.662237437136716'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 439, Energy: -1437.8521675174873, Std: 6.307860233848332'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 440, Energy: -1443.3608246461788, Std: 5.969733762777712'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 441, Energy: -1449.3287175691978, Std: 5.77630313423793'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 442, Energy: -1444.6876865525314, Std: 6.031622385128507'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 443, Energy: -1441.1827130961994, Std: 5.806379333437219'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 444, Energy: -1449.2199845853586, Std: 5.567240448927044'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 445, Energy: -1448.5571806738128, Std: 5.563903013520728'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 446, Energy: -1440.3970623666544, Std: 6.012513406545176'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 447, Energy: -1443.7846339446608, Std: 6.015348946279882'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 448, Energy: -1445.1167272017763, Std: 5.614676061071569'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 449, Energy: -1449.1516292082215, Std: 5.348254901778314'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 450, Energy: -1439.4186793978997, Std: 5.909782341917132'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 451, Energy: -1444.2368337905707, Std: 5.842646038581749'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 452, Energy: -1446.6777271158824, Std: 5.6339112976099965'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 453, Energy: -1440.7261101983343, Std: 5.442218242851333'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 454, Energy: -1453.1528762776743, Std: 5.401162465297231'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 455, Energy: -1441.133219973163, Std: 5.992428301055668'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 456, Energy: -1444.6621069603823, Std: 5.784405597569068'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 457, Energy: -1457.829776878894, Std: 5.331008456955886'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 458, Energy: -1439.3591023056174, Std: 5.840931356419716'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 459, Energy: -1441.5583881973982, Std: 5.724666298462128'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 460, Energy: -1447.571886094159, Std: 5.645227369213515'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 461, Energy: -1440.5129051663714, Std: 5.786289696077037'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 462, Energy: -1445.5800633625308, Std: 5.921574483789'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 463, Energy: -1435.8244612752208, Std: 5.783120663151744'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 464, Energy: -1445.0630577674926, Std: 5.833724376702515'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 465, Energy: -1448.9158278415023, Std: 5.63082503506432'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 466, Energy: -1449.190156831607, Std: 5.904938772398598'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 467, Energy: -1442.7994609542893, Std: 5.672054538456354'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 468, Energy: -1444.621244403225, Std: 5.7636332479762'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 469, Energy: -1443.7390327036292, Std: 5.453986274654787'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 470, Energy: -1448.0971790385681, Std: 5.464157115233137'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 471, Energy: -1449.4657398913018, Std: 5.70417058603013'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 472, Energy: -1446.2919229451738, Std: 5.58469601310965'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 473, Energy: -1440.7330969093728, Std: 5.8980890262207675'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 474, Energy: -1441.5968566777215, Std: 6.055494805902781'"
      ]
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     "data": {
      "text/plain": [
       "'Evaluation: 475, Energy: -1442.0535650641689, Std: 5.845807518850971'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 476, Energy: -1447.3936368554084, Std: 5.491324072769224'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 477, Energy: -1435.108634520071, Std: 5.830419563932683'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 478, Energy: -1452.3049992906192, Std: 5.5234585865803565'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 479, Energy: -1432.791624857122, Std: 6.027686233203227'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 480, Energy: -1444.6672049420322, Std: 5.527156534883163'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 481, Energy: -1449.603983524123, Std: 5.633112844802673'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 482, Energy: -1446.8097832046346, Std: 5.636909416178116'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 483, Energy: -1450.9297890346818, Std: 5.527856992172075'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 484, Energy: -1446.0205359451616, Std: 5.544753091699222'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 485, Energy: -1444.8412399044546, Std: 5.65229112943523'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 486, Energy: -1436.281964518126, Std: 5.937545955767048'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 487, Energy: -1442.4722563788246, Std: 5.906341749502324'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 488, Energy: -1439.1013643790152, Std: 5.878996944281935'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 489, Energy: -1443.00532746902, Std: 5.823675655269992'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 490, Energy: -1453.6857303084744, Std: 5.5563767547243295'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 491, Energy: -1449.8466413099486, Std: 5.659198665169648'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 492, Energy: -1443.7808787191773, Std: 5.741637176928319'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 493, Energy: -1437.2374912264618, Std: 5.8220967874525735'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 494, Energy: -1452.3603914550822, Std: 5.192148275936412'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 495, Energy: -1447.8915323914368, Std: 5.613589474689756'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 496, Energy: -1443.6520477855668, Std: 5.636777403860792'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 497, Energy: -1441.3968365903277, Std: 5.688316649192467'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 498, Energy: -1448.2902825954782, Std: 5.5968935194584315'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 499, Energy: -1443.8455088985281, Std: 5.998464992380561'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 500, Energy: -1439.881816878042, Std: 5.741586263926243'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 501, Energy: -1441.140264708486, Std: 5.6597081094635024'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 502, Energy: -1440.4390148518921, Std: 5.888053098243103'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 503, Energy: -1448.1970628118058, Std: 5.540973325281782'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 504, Energy: -1446.6443324985253, Std: 5.56597846649631'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 505, Energy: -1442.247485754134, Std: 5.472321460952756'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 506, Energy: -1443.8440373630947, Std: 5.741175555857366'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 507, Energy: -1451.7178201921877, Std: 5.615324661210019'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 508, Energy: -1447.8014471667095, Std: 5.605726571193622'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 509, Energy: -1433.988610157543, Std: 6.0332833858244035'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 510, Energy: -1456.0876923466806, Std: 5.340588050930477'"
      ]
     },
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     "data": {
      "text/plain": [
       "'Evaluation: 511, Energy: -1447.30258369379, Std: 5.656203324795169'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 512, Energy: -1431.5264678408346, Std: 5.784119572237018'"
      ]
     },
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     "data": {
      "text/plain": [
       "'Evaluation: 513, Energy: -1429.8371677171835, Std: 6.165509572156577'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 514, Energy: -1452.2676531226164, Std: 5.578427458759369'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 515, Energy: -1448.3195210344488, Std: 5.863658665708602'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 516, Energy: -1451.3728441050657, Std: 5.4525852187918975'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 517, Energy: -1452.5000178088276, Std: 5.741437432767478'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 518, Energy: -1452.6083585988256, Std: 5.663569707045553'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 519, Energy: -1439.5667390443512, Std: 5.957413049344763'"
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     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 520, Energy: -1450.856727567848, Std: 5.680724280248864'"
      ]
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 521, Energy: -1450.827570786751, Std: 5.341798472888919'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 522, Energy: -1446.1966763879022, Std: 5.687662554856937'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 523, Energy: -1445.8728631274207, Std: 5.734155408796309'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 524, Energy: -1443.8501934061403, Std: 5.7026594380280295'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 525, Energy: -1451.8463585496888, Std: 5.751385982745136'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 526, Energy: -1425.8962130822945, Std: 5.811337993120386'"
      ]
     },
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     "data": {
      "text/plain": [
       "'Evaluation: 527, Energy: -1446.2531054963843, Std: 5.8751618271877675'"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 528, Energy: -1446.7636966503203, Std: 5.6947378644333595'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 529, Energy: -1448.2314041150278, Std: 5.449613919614732'"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 530, Energy: -1446.0462262568333, Std: 5.693374335067973'"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 531, Energy: -1441.3829630491016, Std: 5.611550675904841'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 532, Energy: -1444.8992724274488, Std: 5.655244719405749'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 533, Energy: -1448.6060041835726, Std: 5.444848327345954'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 534, Energy: -1442.419744984856, Std: 5.678528596645403'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 535, Energy: -1434.4319472670882, Std: 5.861191825563159'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 536, Energy: -1455.4203389647378, Std: 5.350715943248067'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 537, Energy: -1442.6448820251192, Std: 6.019086801905467'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 538, Energy: -1452.36489943559, Std: 5.722175653191035'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 539, Energy: -1436.6292918897266, Std: 5.996771665064748'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 540, Energy: -1446.03011531461, Std: 5.8617687114525685'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 541, Energy: -1437.807751743927, Std: 6.066563169624498'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 542, Energy: -1440.4625989776023, Std: 5.560845383862984'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 543, Energy: -1446.8343373868008, Std: 5.823460552705517'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 544, Energy: -1441.998904914704, Std: 5.816172688031971'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 545, Energy: -1441.6278103876937, Std: 5.820843145100178'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 546, Energy: -1449.126232160467, Std: 5.8708040536252195'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 547, Energy: -1441.415841456202, Std: 5.574819788713795'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 548, Energy: -1451.9285434073604, Std: 5.631763681487202'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 549, Energy: -1442.6031326461884, Std: 5.674812832751686'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 550, Energy: -1441.3520495202163, Std: 5.7602394964720105'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 551, Energy: -1439.2805763370852, Std: 5.807999599872008'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 552, Energy: -1437.9734551729723, Std: 6.048281924679577'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 553, Energy: -1440.2240270728687, Std: 5.861846343453301'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 554, Energy: -1439.4549221606815, Std: 5.633819598296547'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 555, Energy: -1432.8938847222257, Std: 5.980180937337635'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 556, Energy: -1454.8085470740407, Std: 5.567696601779771'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 557, Energy: -1446.4316695479436, Std: 5.788705547640402'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 558, Energy: -1437.7958154368412, Std: 5.897940323152032'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 559, Energy: -1443.8579708947102, Std: 5.798607021449024'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 560, Energy: -1439.4502140602833, Std: 5.6858974968537'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 561, Energy: -1441.1710385834751, Std: 5.667506121820227'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 562, Energy: -1441.987325066605, Std: 5.758602851137023'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 563, Energy: -1444.6956655519566, Std: 5.63682897339279'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 564, Energy: -1442.3046352056567, Std: 5.657920542727612'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 565, Energy: -1447.246606414164, Std: 5.875403409484166'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 566, Energy: -1451.3137398807507, Std: 5.278751457679728'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 567, Energy: -1450.3833169590519, Std: 5.706690035259103'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 568, Energy: -1451.260438011552, Std: 5.48827982227166'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 569, Energy: -1445.4200583889046, Std: 5.64313896348903'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 570, Energy: -1453.9433030340058, Std: 5.669379690136816'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 571, Energy: -1441.2700318295633, Std: 5.961166518985125'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 572, Energy: -1434.7054446151199, Std: 6.057234219559115'"
      ]
     },
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 573, Energy: -1449.9526239415675, Std: 5.508248170483143'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 574, Energy: -1439.7076852009916, Std: 5.855151066769354'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 575, Energy: -1451.6178510316768, Std: 5.697583459393694'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 576, Energy: -1442.1101791075591, Std: 5.457687057919289'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 577, Energy: -1451.68856532775, Std: 5.434363411469422'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 578, Energy: -1443.6734435522412, Std: 5.765802411733337'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 579, Energy: -1445.306068126168, Std: 5.804054849454846'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 580, Energy: -1446.3647325387246, Std: 5.758185183430296'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 581, Energy: -1442.7341267588479, Std: 5.67311641557671'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 582, Energy: -1448.4948780184734, Std: 5.587583862191865'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 583, Energy: -1444.7069839399835, Std: 5.518133057927383'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 584, Energy: -1443.633759055418, Std: 5.868868128346576'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 585, Energy: -1444.2634929455414, Std: 5.663091504536006'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 586, Energy: -1447.8301288097762, Std: 5.502336328926233'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 587, Energy: -1447.240140684868, Std: 5.74781780874312'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 588, Energy: -1441.9133378434108, Std: 5.873032714360121'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 589, Energy: -1435.5346860157993, Std: 6.136560702198237'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 590, Energy: -1444.3304722947976, Std: 5.527105833632618'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 591, Energy: -1439.948018198329, Std: 6.021242744790128'"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 592, Energy: -1439.312018539418, Std: 5.978596849256258'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 593, Energy: -1446.3684066013436, Std: 5.780214998142698'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 594, Energy: -1439.6346361373344, Std: 5.828776642452628'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 595, Energy: -1438.7290255171922, Std: 5.5802040908423365'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 596, Energy: -1438.3289250253342, Std: 5.8277769524350225'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 597, Energy: -1443.008093050907, Std: 5.9015046387553465'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 598, Energy: -1450.3636727759476, Std: 5.689955622655534'"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 599, Energy: -1441.9400255045125, Std: 5.695836765116462'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 600, Energy: -1446.1773642189457, Std: 5.9464064192031945'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 601, Energy: -1447.2365092278187, Std: 5.623922362818429'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 602, Energy: -1430.331111162678, Std: 6.0839533764831355'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 603, Energy: -1432.599299435793, Std: 6.134042755042345'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 604, Energy: -1446.209629717615, Std: 5.6279592387993045'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 605, Energy: -1451.5503354931916, Std: 5.3551904743890635'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "'Evaluation: 606, Energy: -1446.7233078637596, Std: 5.731683519602523'"
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       "'Evaluation: 607, Energy: -1454.655083671978, Std: 5.399961524301089'"
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       "'Evaluation: 608, Energy: -1445.2809511703629, Std: 5.795362128697035'"
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     "data": {
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       "'Evaluation: 609, Energy: -1441.3471998202447, Std: 5.699986369181469'"
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     "data": {
      "text/plain": [
       "'Evaluation: 610, Energy: -1442.6062210843993, Std: 5.804627702163907'"
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     "data": {
      "text/plain": [
       "'Evaluation: 611, Energy: -1449.5945625248448, Std: 5.823878837394763'"
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     "data": {
      "text/plain": [
       "'Evaluation: 612, Energy: -1437.5765742686433, Std: 5.790789300626004'"
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     "data": {
      "text/plain": [
       "'Evaluation: 613, Energy: -1431.7663240236586, Std: 6.12728807596644'"
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     "data": {
      "text/plain": [
       "'Evaluation: 614, Energy: -1442.871077534153, Std: 5.659129840243509'"
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      "text/plain": [
       "'Evaluation: 615, Energy: -1442.895406966496, Std: 5.588477169895075'"
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     "data": {
      "text/plain": [
       "'Evaluation: 616, Energy: -1460.3833958821729, Std: 5.311117548209026'"
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      "text/plain": [
       "'Evaluation: 617, Energy: -1436.9616694404044, Std: 5.8812743302528805'"
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      "text/plain": [
       "'Evaluation: 618, Energy: -1447.1805742546792, Std: 5.658245667971526'"
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      "text/plain": [
       "'Evaluation: 619, Energy: -1439.294288236199, Std: 5.740344002675136'"
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      "text/plain": [
       "'Evaluation: 620, Energy: -1445.832785233822, Std: 5.8461135943957085'"
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     "data": {
      "text/plain": [
       "'Evaluation: 621, Energy: -1444.950566843545, Std: 5.814870304307304'"
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      "text/plain": [
       "'Evaluation: 622, Energy: -1453.4759218488616, Std: 5.394534242457769'"
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      "text/plain": [
       "'Evaluation: 623, Energy: -1445.967684700929, Std: 5.575663934546815'"
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       "'Evaluation: 624, Energy: -1441.2849085809207, Std: 5.949729970080749'"
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      "text/plain": [
       "'Evaluation: 625, Energy: -1448.6184986446997, Std: 5.333597815969901'"
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      "text/plain": [
       "'Evaluation: 626, Energy: -1446.7092263445786, Std: 5.632209914619492'"
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      "text/plain": [
       "'Evaluation: 627, Energy: -1449.4928674838486, Std: 5.680275912907223'"
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      "text/plain": [
       "'Evaluation: 628, Energy: -1438.913022939431, Std: 5.7083017706704835'"
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      "text/plain": [
       "'Evaluation: 629, Energy: -1447.5958796191262, Std: 6.007874346966285'"
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      "text/plain": [
       "'Evaluation: 630, Energy: -1439.9013321592076, Std: 5.757408771915229'"
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      "text/plain": [
       "'Evaluation: 631, Energy: -1437.0553527368756, Std: 5.946912857947143'"
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      "text/plain": [
       "'Evaluation: 632, Energy: -1438.2519902444387, Std: 5.792646446353225'"
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      "text/plain": [
       "'Evaluation: 633, Energy: -1440.2054197481013, Std: 5.706432881590742'"
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     "data": {
      "text/plain": [
       "'Evaluation: 634, Energy: -1442.5483341155887, Std: 5.92146196875687'"
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      "text/plain": [
       "'Evaluation: 635, Energy: -1450.8436873298751, Std: 5.599894031937659'"
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      "text/plain": [
       "'Evaluation: 636, Energy: -1442.6485645300684, Std: 5.687865366945055'"
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      "text/plain": [
       "'Evaluation: 637, Energy: -1439.6279072854898, Std: 5.8873427637647255'"
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      "text/plain": [
       "'Evaluation: 638, Energy: -1458.1543204501181, Std: 5.620083149689852'"
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      "text/plain": [
       "'Evaluation: 639, Energy: -1438.0005606980192, Std: 5.795683355968189'"
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      "text/plain": [
       "'Evaluation: 640, Energy: -1448.6227821013752, Std: 5.45216995745262'"
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       "'Evaluation: 641, Energy: -1452.7668497580057, Std: 5.71901830281823'"
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      "text/plain": [
       "'Evaluation: 642, Energy: -1451.3182681871547, Std: 5.4883981884523045'"
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      "text/plain": [
       "'Evaluation: 643, Energy: -1435.1006827517308, Std: 5.869236978103957'"
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      "text/plain": [
       "'Evaluation: 644, Energy: -1445.1761047140392, Std: 5.691171858302681'"
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      "text/plain": [
       "'Evaluation: 645, Energy: -1448.1791324654262, Std: 5.728241363479699'"
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       "'Evaluation: 646, Energy: -1442.1256598638474, Std: 5.586848219380646'"
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       "'Evaluation: 647, Energy: -1447.7971375044694, Std: 5.634354269936911'"
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      "text/plain": [
       "'Evaluation: 648, Energy: -1441.4116837103552, Std: 5.769023567352068'"
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      "text/plain": [
       "'Evaluation: 649, Energy: -1445.484445538693, Std: 5.729840396059252'"
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       "'Evaluation: 650, Energy: -1442.5152010312545, Std: 5.574874032979492'"
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      "text/plain": [
       "'Evaluation: 651, Energy: -1441.466908022484, Std: 5.803146838619684'"
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       "'Evaluation: 652, Energy: -1451.3061884357103, Std: 5.726695852522706'"
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      "text/plain": [
       "'Evaluation: 653, Energy: -1446.636681338891, Std: 5.522374746904452'"
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       "'Evaluation: 654, Energy: -1442.5233269123867, Std: 5.686978550875259'"
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      "text/plain": [
       "'Evaluation: 655, Energy: -1447.848111335236, Std: 5.494863191175052'"
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       "'Evaluation: 656, Energy: -1451.1822682270576, Std: 5.743042400747439'"
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       "'Evaluation: 657, Energy: -1439.6799140899195, Std: 5.589061601965144'"
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       "'Evaluation: 658, Energy: -1434.9831934484343, Std: 5.900249383831991'"
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       "'Evaluation: 659, Energy: -1445.7105520666573, Std: 5.5008476559189825'"
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       "'Evaluation: 660, Energy: -1453.8728833437112, Std: 5.49081711738902'"
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      "text/plain": [
       "'Evaluation: 661, Energy: -1439.8557503512754, Std: 5.840664359363574'"
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       "'Evaluation: 662, Energy: -1445.476455905734, Std: 5.635432870499822'"
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       "'Evaluation: 663, Energy: -1436.0138856893027, Std: 5.7696750289864385'"
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       "'Evaluation: 664, Energy: -1441.0450399415959, Std: 5.89982903507401'"
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       "'Evaluation: 665, Energy: -1457.1584233630404, Std: 5.473807001717342'"
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      "text/plain": [
       "'Evaluation: 666, Energy: -1448.3309854757063, Std: 5.6860786132143755'"
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     "data": {
      "text/plain": [
       "'Evaluation: 667, Energy: -1452.790009104923, Std: 5.6525848374570655'"
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      "text/plain": [
       "'Evaluation: 668, Energy: -1442.3128475827755, Std: 6.041646423523084'"
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      "text/plain": [
       "'Evaluation: 669, Energy: -1430.5837290847262, Std: 6.145700378354686'"
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       "'Evaluation: 670, Energy: -1444.7257198730704, Std: 5.95057124853032'"
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       "'Evaluation: 671, Energy: -1445.5511479634024, Std: 5.82467337165172'"
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       "'Evaluation: 672, Energy: -1448.8888591581285, Std: 5.751964229512777'"
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       "'Evaluation: 673, Energy: -1442.3499472766375, Std: 5.947225579761965'"
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       "'Evaluation: 674, Energy: -1441.268283730433, Std: 5.689710936210154'"
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       "'Evaluation: 675, Energy: -1444.574070341886, Std: 5.520479801054821'"
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       "'Evaluation: 676, Energy: -1437.7095112899776, Std: 5.893200998737841'"
      ]
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       "'Evaluation: 677, Energy: -1451.3328413286417, Std: 5.52242565660996'"
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       "'Evaluation: 678, Energy: -1438.9510139524436, Std: 5.777930946503171'"
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       "'Evaluation: 679, Energy: -1455.5186736263192, Std: 5.576291872562669'"
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      "text/plain": [
       "'Evaluation: 680, Energy: -1447.2861992261376, Std: 5.5765966035553864'"
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       "'Evaluation: 681, Energy: -1444.9373393613193, Std: 5.7350269956872815'"
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       "'Evaluation: 682, Energy: -1436.66212657459, Std: 5.7444318167125825'"
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       "'Evaluation: 683, Energy: -1438.489889904161, Std: 5.978756082629769'"
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       "'Evaluation: 684, Energy: -1444.7740874200524, Std: 5.718728665965364'"
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       "'Evaluation: 685, Energy: -1444.1798818186671, Std: 5.9214102248479215'"
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       "'Evaluation: 686, Energy: -1448.2840132645413, Std: 5.726245029560079'"
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       "'Evaluation: 687, Energy: -1441.9307409917258, Std: 5.5159410585959225'"
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       "'Evaluation: 688, Energy: -1448.1756346678126, Std: 5.848506883206954'"
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       "'Evaluation: 689, Energy: -1443.0306967149497, Std: 5.649905325654284'"
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       "'Evaluation: 690, Energy: -1440.9733803579445, Std: 5.820999229170347'"
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       "'Evaluation: 691, Energy: -1442.972024927542, Std: 5.698392061206548'"
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       "'Evaluation: 692, Energy: -1439.4557095267646, Std: 5.735368768645276'"
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       "'Evaluation: 693, Energy: -1437.5633166587052, Std: 6.125340437593575'"
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    {
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       "'Evaluation: 694, Energy: -1439.122930348473, Std: 6.0301548341864235'"
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       "'Evaluation: 695, Energy: -1452.3888666419857, Std: 5.317368487190862'"
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       "'Evaluation: 696, Energy: -1445.7044787990503, Std: 5.9389173542045'"
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       "'Evaluation: 697, Energy: -1449.4362396144122, Std: 5.54109370845907'"
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    {
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       "'Evaluation: 698, Energy: -1447.3897320340152, Std: 5.5337084496052515'"
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    {
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       "'Evaluation: 699, Energy: -1434.9330678393617, Std: 6.078745465521624'"
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    {
     "data": {
      "text/plain": [
       "'Evaluation: 700, Energy: -1447.5286460504326, Std: 5.477387347101077'"
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    {
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      "text/plain": [
       "'Evaluation: 701, Energy: -1440.3430826602812, Std: 6.100110169889569'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 702, Energy: -1441.80112700996, Std: 6.0252989345166865'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 703, Energy: -1458.031540807341, Std: 5.754001822968879'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 704, Energy: -1444.602128345774, Std: 5.4893913911899865'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 705, Energy: -1446.0746262424047, Std: 5.642930738666472'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 706, Energy: -1453.1240016176362, Std: 5.759050935834787'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 707, Energy: -1437.8518945459239, Std: 6.106679440904678'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 708, Energy: -1440.9668937415531, Std: 5.468182099427107'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 709, Energy: -1450.1002674646247, Std: 5.891998106257499'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 710, Energy: -1455.2465626250653, Std: 5.509445473054731'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 711, Energy: -1440.3950193292735, Std: 5.613253707277754'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 712, Energy: -1446.578599729166, Std: 5.85305345594429'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 713, Energy: -1447.622328045406, Std: 5.684366426205071'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 714, Energy: -1446.2873049994896, Std: 5.707667771958198'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 715, Energy: -1449.7752542991618, Std: 5.614076916083349'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 716, Energy: -1446.4824471345198, Std: 5.508318405101289'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 717, Energy: -1455.1775214094794, Std: 5.655256096500045'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 718, Energy: -1445.116404507336, Std: 5.9017896577339855'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 719, Energy: -1443.9158460532008, Std: 5.755497870508255'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 720, Energy: -1444.6921097710228, Std: 5.865244741970304'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 721, Energy: -1443.7409586219255, Std: 5.581840377684082'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 722, Energy: -1452.212916811564, Std: 5.428836315536463'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 723, Energy: -1444.544545204192, Std: 5.635895996131793'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 724, Energy: -1442.2061624569697, Std: 5.790979782688421'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 725, Energy: -1444.6326669813523, Std: 5.730397229630459'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 726, Energy: -1455.1328551768527, Std: 5.4265383722152585'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 727, Energy: -1446.3963007611992, Std: 5.594426534396767'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 728, Energy: -1437.364704908988, Std: 5.724598314181829'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 729, Energy: -1440.2148827282197, Std: 5.747825851432863'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 730, Energy: -1449.8524766242454, Std: 5.563567836097566'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 731, Energy: -1446.6950563678142, Std: 5.464273716152788'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 732, Energy: -1443.437841966377, Std: 5.719424415952649'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 733, Energy: -1449.645245485073, Std: 5.549698211065908'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 734, Energy: -1440.552409482546, Std: 5.768719426689741'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 735, Energy: -1448.1440059958566, Std: 5.476835738004015'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 736, Energy: -1448.17894801753, Std: 5.60667054888842'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 737, Energy: -1454.1741999288383, Std: 5.447477838242026'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 738, Energy: -1439.8977010533526, Std: 6.053603490961995'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 739, Energy: -1445.5919297094651, Std: 5.704135417631006'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 740, Energy: -1434.7161326769863, Std: 5.73736999797951'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 741, Energy: -1450.7932851649678, Std: 5.6685833342865255'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 742, Energy: -1449.1684802036282, Std: 5.758407369826878'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 743, Energy: -1436.007715032173, Std: 5.822944217907337'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 744, Energy: -1449.917406792247, Std: 5.610142895985558'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 745, Energy: -1450.3305767317322, Std: 5.661150840847504'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 746, Energy: -1461.7086136073333, Std: 5.343260178334407'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 747, Energy: -1446.3147687294318, Std: 5.671364048096409'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 748, Energy: -1443.5510449480519, Std: 5.789026908106437'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 749, Energy: -1445.5152676128914, Std: 5.797002187215568'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Evaluation: 750, Energy: -1449.6588633784709, Std: 5.441292164486502'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ansatz15 = TwoLocal(qubo_15vars.get_num_vars(), rotation_blocks='ry', entanglement_blocks='cz')\n",
    "sol15_qasm_vqe_cobyla = Optimization_VQE(qubo_15vars,ansatz=ansatz15, local=False, backend=provider2.backend.ibmq_qasm_simulator,\n",
    "                                       provider=provider2, optimizer=COBYLA(maxiter=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "86a18cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3o8/OoM8z98S6lGanKAq33baId94pxOUKNvj9Wq2KzEwzK1deTYcOyVGosHEk+ISIouode1h10bV0vOFKuvz5+ric9hF1++3evlAozNKlxaxcWcKyZcU4HF60WjV9+mQwfHg7zjqrA23bxtcIp7GeeWYVjz66HLe74aF3mEajIi8viS1bpmI2x8dMhwSfEFHiXLOZ1eNvoscjd9Bh2uWxLkc0wf63P2LHv2azOv9PPP/PtXg8QXy+4DHPuaxWHcFgmDPP7MDjj5/G4MGJuyJ061Y7gwdPx+NpfOgdZjJpufbafrz00tkRqKzpJPiEiALbN9+z7uq76P/a47QZFx9/2EXjrVpVwrgz3qIibMAbqN97TCYtt9wygCefHInBoI1ugVEwcuRMli8/wPES4pZbBnDjjSfTqVPN9GVhoZ0nnljBggW7a72fyaRl3bpr6Nkz9udKysMGISKsZM4XrLv6LgbNekFCrwUoKNjJmWfOptRX/9AD8HiCvPrqekaNmo3L5Y9egVGwY4eDNWsOHjf0AIqKqrjnnsUMGjSdIUNm8PXX+ygoGEf//pm13jMYDPPCC2ujVHHDyIhPiAja+/r77HzqVYbOfYOUAb1jXY5oom++2cfYsR836RmX0ahh6NC2fPvtBNTqxHjGe//9S3juudUEAsffsnA8dvut3HffEt54Y2Ot11gsOqqq/hDzZ90y4hMiAhRFYeeTr7D7H/9h+NfvSei1AE6njyuu+KxJoQfg9YZYu/YgL7ywJkKVRd833+yrd+ip1SomTOiJ1apn+fIDJ7z+xx8rmlhd00nwCdFESjhM4R+foPTjLxn+7UwsXTvEuiQRAXff/R3V1bVPUVqtOp5//kx++ukG3O47WLbsKoYMOf5iFpcrwIMPLqW4uCpa5UbU1q32E17Tr18mVVV/wOe7k3/96xzGj5/L5s22Ot+j0ajYsKEsUmU2mgSfEE0Q9vtZP+X/qNq0nVO/mo6xbXasSxIR4HT6ePfdLXi9tZ/Q8Oab53HeeZ2YMuVz+vd/hy+/3MuiRZfXulk7FFJ47bX1Uao4suozyt2+3cGAAe8ybNh7vPbaBt5553z69q39GR/UPOerqPBFqsxGS7ylRkLEiWC1izVX/AGNUc8p899EYzLGuiQRIbNmbaOuPgNGo5bLLuvBZZfN5bvv9gPw6KPLufjiLtx888k89NCyY97j84V49dX1PP74yGZ5xhUIKnh84PYpePwKXh94fAoe/8+v+Wp+fvg1z69eCysnri8QCB+Ztly79iBDh7bhzjsHc911/6v1PWq1Cp0u9uMtCT4hGsFvc7D6khux9ulG/389jlorf5Rakv/9b0+dox6tVoVWqz5mROjxBBk5Mq/W9/l8IX76yUnnzqknrEFRFAJBcPvA468JJe/P/9vtOzqoan7+q9f8oChgMoDJoMJkUGHWH/7fNf+dZlXTLuPo10x6FWYDzHzSwp49lfX+5wU1oWYwaE54TZcuKQ26bzTIn1YhGsiz7wArL7qWNhefRc8n/xTzFWoi8lavPljnz6urAyxfXsyDD57K5s02SktdXHVVL4YPb8euXRW1vk+tVvPJghJOP9ta54jrcJCpVL+El1n/q4AygEmvIiNZ/avQ+iXkTAbQaWj0781hw9rVGXx//evpzJ+/m/37q0hK0jNxYm9Gj27PRRd9XOd9PZ4gAwbE/nGABJ8QDVC19UdWj72OTrdNpsud02JdjoiS8nLvCa+ZPHkBb711PsXFNxEMhlm79iDvv7+NwYNrP1/R5w+xfms1eX3DR4IqK1X9q9D6VXjpQaeNzV+qxo/vxrx5P1JdffyNi23aWJgx4yLatDHjdPrZuLGMCy6Yw5df/lTnffv1y8Rq1Ueh4oaR4BOinspXbmDNZbfQ66//R97k/FiXI6KoPgOl3budjB49G7NZR3KyntJSF7NmjWX3bmet79FqVAzvq+OK0bH/8q9Lfn53brjhy1p/PnXqFw2+p9Wq4+67T2lKWRET+6eMQiSAsi+X8EP+jfR//QkJvVYgPb3+ZyW63QFKS12kpho477xOzJ27q9ZrdTo1bdrE//FGer2Gv/xlOBZL5JpKp6cbufTS7hG7X1NI8AlxAsWz5rF+6j0M/vBlci46M9bliGZwyiknbi597rmdOP/8znTqlMLZZ3fkm28msG2bg7ff3lzrewKBcJ1TofHkjjsG0717KhpN06dbTSYtH354CXp93YtfmosEnxB12PPydLbd+wzDvnib9JFDYl2OaCYXXtgZq7Xu0U5KioGXXz6Lbdum8u67F7B0aTHnnfcRwWDtHU8sFh3t2ydFutyo0GjUzPogH2uSoUmt1sxmLY89dhqnnNI2gtU1jfTqFOI4FEVhx6MvUvLBAk5Z8B/MnWpfoi5aHpfLT3b2q01uV/ZrRqOG++8/lYceGh6xe0aT26ewZrefcGUVV+Z/SEWFt84N/cdjMtWE3p//PDRKVTaOjPiE+A0lFGLzbY9waMF3DP92poReK2Sx6Ln++pMwmSK3/k+jUXPjjSdF7H7R5PKFWfOjn05ZWkYNy2THjmu58spemEzaek19Wq068vKS+PrrK+Iu9EBGfEIcJeTzs37Knwk4nAz+6BV0yS3jJG3RcC6Xn+7d/0NJiavJ97JYdPzjH6O54YaTI1BZdFV7w6zdHaBrjpbcjKOfyW3YcIjnn/+B2bO3o9WqUalUBINh1GrQatW43UF69kzn3ntP4Xe/6xG35xBK8Anxs2BVNT9cdiu6tBQGvPMsGqMh1iWJGFux4gBnnfVBk6Y8TSYto0e3Z/78S+O+2UG1J8ya3QG6t9XSLr32hSihUJht2xxs2FCG0+lDp1PTtWsqAwdmk5oa/637JPiEAHyH7Ky++HpSBvej30sPo9LEx+ozEXuLFu0lP78AtztQ68GstTGbtZx2Wi6ffTY+bkc/h1W6w6zbE6BnOy1t0lr27395xidaPfdPRXw/eiJZF4yi3yuPSuiJo5x9dkdWr55Enz4Z9d7XptGoMJm0PPDAqSxYcFnch57z59DrldvyQw9kxCdaiEAgxK5dFbhcAQwGDV27pmI2n/hLqmrzDlZdfD1d/nQdnW+b3AyVikQVDIZ5661N/O1vqzh40I2iKEdNgWo0KiwWHX5/iN/9rgcPPjicnj3TY1hx/VS4wqz/KUDfPC1ZKS0/9ECCTySw8nIvb7+9mX//eyM//liBwaBBrVahKDXNcNu1s3LllT259daBdOiQfMz7HcvWsOaK2+nz9/vJvXJsDH4FIhEpisLGjWWsWlXKihUHsNk86PUa+vbNYOjQNowcmUdKSmI8Hy6vDrNhb4B+7XVkJreeCUAJPpFwgsEwzzyziscfX4FaXfehmXq9BrUarryyFy++eBZJSTU9Eg8u+JaN197Lyf99huzzzmiu0oWIG/aqMJv2BujfUUdGUusJPZDgEwnm4EEX55zzIbt3V+By1X+lndGoITnZwJdf/o6MjcvZdt+zDJ7zKmnD4n95uRCRZqsMs3l/gJM66ki3tq7QAwk+kUAOHXIxZMgMSkqqCQYb99vWYoC/Zyxm4qLnSerdNcIVChH/ypwhCouCDOikI9XS+kIPZFWnSBDhsMLYsZ80KfQAXD6FBzznEG4n3VhE63OwIsSWoiADO7fe0AMJPpEgXnppLVu22JsUejVUuDxBbrllYUTqEiJRlJaH2FZcE3op5tb91S9TnSLueb1BsrNfoarq+KdB79lzPZ06pRzz+vz5uxk79uPjvsdo1LJhwzX06BH/y82FaKoDjhA7S4IM6qIjydS6Qw/kBHaRAD78cHudHTOGDp1xVOPctm2trFkzmQ8+2F7re0KhMC+8sJZXXjk7kqUKEXeK7SF+PBhkcFcdVqOEHshUp0gA//1vIdXVxx/tAdhsHg4edB/5z4UXdqay0ldn8AUCYWbP3haNcoWIG/ttIXZL6B1DRnwi7q1bd6hB1197bX9mzNiK11v3doeqqgA2m5vMTHNTymv1gsEwO3eWU1HhQ6NR0alTMtnZlliX1ertKwuytyzE4K56zIb4bo7d3CT4RFyrrvZTVeWr9/XnnNORLl1S+fe/N57wWpNJw9atDk4/XYKvodzuALNnb+PFF2sWHen1GjSamq45Pl8Is1nH+ed34q67hjBkSJtYl9vq/HQoSJE9xJBuekx6Cb3fkuATcc3rDaLRqAkG63fy8/XXn8SqVSVs3FhWj6tVJxwViqMpisJ7723lllsWoSjKkSlovz981HU+X4jZs7czd+4uBgzIZubMi+jY8dgFSCLydh8MUlIeZkhXPUYJveOSSV8R10wmLaFQ+MQXAllZZsaN61av0V4NJaInbLd0Hk+AsWM/5qabFlJV5a/zuSvU7L10u4OsXFlC375v8+GHtT9zFU2nKAq7SoOUlocZ0lUnoVcHCT4R1ywWfb0Ptvz97/vi84V4//36LVrxeIL06ZPRlPJaDa83yJgxH/D11/twueoOvN8KhRRcriBTpnzOzJlbolRh66YoCrtKQpQ5a0LPoJPQq4sEn4h7gwZl1+u6667rz6xZ2+r9xZySYiA93dSU0lqN2277ig0byvB66zflfDweT5DrrvuSTZvqMw0t6ktRFHYcCGGvDjO4qw69hN4JSfCJuDdtWn+Skuo+W2/06Pb06JFe72lOnU7NhRd3oPigg2q3F+njULtvv93HzJlb8Xia/jzU6w1y+eWfEgg0PkDFLxRFYVtxkAp3mMFddOi1Enr1IZ1bRNzz+0NkZ7+K01n/1Z0nYjRq2LhpChmZepxVboKhMClJJlKSzBj09Ttlu7U46aT/smmT7bg/U6tVPPLICCZN6kPbthZKSly8994WHnlkOaHQ8b9arFYdb755HhMm9Ipm2S2eoihsLQpS7VUY2EWHTiOhV18y4hNxT6/X8Nxzo7BYIhNIRqOWyZP70L1bOumpVjq3z6Z92wwUBfYesLGn6BDlzup6L6ppydavP8SPP1bU+vN77jmFW28dwB/+8BW9er3FHXd8za23DuS++4bV+p7q6gB/+9uqKFTbeiiKQuH+IC6fwiAJvQaTJW0iIVx7bX+mT9/CihUl+P2NnyZTqSA11cDzz5951OtGgw6jIYXsjGRcbh8VVW4OOSqxmAykJJmxmo2oVK3vy2XOnB11/vMeMaIdn322m3nzdgOwd28ln376I8OGta3zvoWFNmke0EhhRWHzviCBoMKgzrqj2vWJ+pERn0gIKpWKgoJ8OnRIQq9v3G9blapmQcs331yBxaKv9XOsFiN5bdLp1qENFpMRe3k1O/eWctBWgdfXsBWNie677/bXeSLG0qXFnHlme3r2rGn23bt3BmPGtGfBgj113tdk0rJ2bcM68oiaLSKb9gYJhhQGSOg1moz4RMJISzOyatUkLrroYzZuLGvQsnqzWUtmpokvv7z8yJf0iWg0atJSLKSlWPD5AzirPOwvtaNRq0lNMpNsNaHVahr7y0kIO3aU1/nzv/1tFUlJerZsmUooFEan0/DEE9/z2mvr63yf1xti2zYH557bKXLFtnDhsMKGvUFUwIBOOtRqCb3GkhGfSChpaUaWLbuKZ5+teeZntdb93M9k0mI0arjxxpPZvv3aeofebxn0OrIzkunWIYecjBS8Pj8/7j/I/hI7ldUewi10jdiJppUnTOjJNdf0YeLEeQwaNJ3Jkxdwyy0DmDatX53vC4XC0jWnAUJhhfU/BVCr4KSOWgm9JpIRn0g4KpWKm28ewJQpfZk9extvvLGRjRvLUBTQaFSEwwrBoEKPHmlcfXVvrruuf8SeJalUKixmAxazgVA4TFW1h3JnNaVlFSRba1aFGg26FvM80GDQArWvpn322VE899wPzJ5d05Vl82YbHTsmc999w3jrrc21vk+rVWM2y9dPfYRCNaGn06ro10GLuoX83ool+Z0nEpbZrGPq1P5MndofRVEoKXHhcgXQ69Xk5SWh0UR3QkOjVpOabCE12YI/EMRZ5ab4oAOVSkVKkpmUJDO6BJ8K7dMng9JSV60/N5t1x2xbCIXCJxyRGAwa+vbNjEiNLVkwpLBuTwCTXkXf9toW8xeqWJPgEy2CSqWiXTtrzD5fr9OSlZ5MZloSHq8fZ5Wb3fsPYjLoSUkyk2QxolZHN4h9u3fj2bAR7+ZNBO0OVDot+i5dMfXrh2ngQDTWhh8VNHp0e5YsKSIQOP7Wjs8++5F77z2FPXucFBbaGDgwm7vuGsK779bdmsztDjJoUE6D60lE+/dXsmRJMd9/f4Aff6xAURTatbMyYkQup53Wjl69jt82LxBSWLc7gNWooneehF4kyQZ2IaIkHFaocnlwVrnx+PwkWUykJpkxGfUR+xJTwmEq583D9vobBIqLQa1Gcbt/uUCrRW00ogSDJF9wAZk33oC+U6d633/7dgcDBrxTa6syq1XH44+PZPz47mRnmygpcTFr1jYee+x7fL7anw+efnouixdfVe86EtF33+3nkUeWs2LFAXQ6NdXVAX79bWux6FAUhW7dUnnwweH87nc9jvy+CAQV1u4OkGxW0StXQi/SJPiEaAaBYIjKKjcVVW4URTkyFarXNX7Sxb93L0V33oV/zx4Uj+fEb9BoUOl0ZN56CxnTpqHS1D0NG/b7qF78JaMnr2NdqQmIzJev1apj5syxXHxx14jcL95UVvq47bavmDNnB253/RbwWCw6hgzJ4b33LiIrx8ra3QHSLGp6tNNI6EWBBJ8QzUhRFLy+AM4qN5XVHgx6bc1UqNWEpgFToe4ffmDfDTeieL0QbliHGZXJhHnQIPJeexW1/tj9jOGAn+rv/ofzi4/Rd+rOns7nMebyJRHp1anRqBg0KIcVK65ukSsTDx50MWLETA4cqG5wQ2+tVo3VquOld3/HsMFZdGsroRctEnxCxEhYUah2eXFWuXF7fVjNRlKTzJhNhjq/8LxbtvDT1ZPqN8qrhcpowDLsVPL+9dqRzwoH/FQvWYjz8znoO3Qh9ZKrMHSsGZXde+9iXnppbb1HMLWxWHRs2jSFzp1Tm3SfeFRd7WfAgHfZu9dZ56b/E0lOMbBh/TV06iQH90aLBJ8QcSAYDFFZ7aGiyk0oHCbFevyG2WGfjx8vuIDggZImf6bKZCLn/vtIzc+naulCnAs+Qt++C6mXTMDQqftR14ZCYS67bC4LF+5tdPiZzVoKCvI555xOTa49Hl133f94770tTTq6CWpGxUOGtGH58oktclQcDyT4hIgzh6dCndVu9FoNKUkWkq0mNBo1h/7+DxzTp9dMcdYi7aqrSL92GtqsLHy7dnHwqb/iWbPmuNeq9HpMo0/C2LV7zQivc/fjXgcQDIa59dZFzJixpUHhZzBoMJu1fPJJPqNGta/3+xLJihUHGDPmg3pNB9977yn89a9n8PLL67j99q+Oe43FouPFF8cwbVr/SJcqkM4tQsQdo0FHTmYK3Tu2ISMtGZfHx659pezfW4Jjxow6Qy/pggvIuf8+7K+/wZ7xl+JZt44Ob7yOtu3xm0YroSCGrG7k3PGXOkMPap5Bvf76uXz22aXk5lpPeEaiXq/BaNQwblxXdu26rsWGHsCTT66oVyeaYcPacsMNJ7NhQ919Sl2uAI8//r2cExklEnxCxCmVSkXSrxpma1Ys40Rfgxm/n0LFJwVUfPgh/t27OfjEkwTLbKRddeXx3xAKUzn/iwZ9wY4Z04F9+27ko4/GMXZsF7KyTOh0aiwWHWazFp1OTa9e6dx552B27LiW2bMvadEn3dtsbhYu3MuJ/hEmJ+t5772LmDbtC8rLT3y2pM3mYeXKpk9pi2PJBnYhEoBGo0b5YSXUtaBFp8PYty/2t94+6uXqZcswDRxY69vCXi+B4mL0eXn1rketVnHuuZ2ONJl2On1UVHjRaNTk5JjR6RK7Y01DrFxZgtGoqXPfIsAbb5zLRx/t4Ntv9/Pwwye+r98fZvHiIk49tV2EKhWHyYhPiATh2bCxzp9r01JRabUE7fajXg/Z7Wgza28PptJq8W4ubFJtKSkGOnZMIS8vqVWFHsDq1aUnPCnkuuv6/7xRfWm97+v3h/juu/1NLU8ch4z4hEgQIYcjKvdV/H6CZWVRuXdrsG9fVZ3bF3r0SOOpp05n5Mj3CQYbtufywIHa+6SKxpPgE6KFCJZXoASDaDOO7v2oycggaLPV/WZZRNFoJ3o+Onx4O7KyzBQWTj3ymlar5owz8rjpppOxWF6o9fgnWdwSHRJ8QiQITXIy4erq2i8IBPAWFmI5bQRV//vfkZctp42g6ssva3+fTocmvXHnFAro2DEZjUZ1zCkVhxUU7KJfv/8e9drbb5/Pzp3lPPXUyjrPPIxl4/WWTIJPiARh7NePwIEDdV5j/+875P7tabwbN+Feu5a0Kyegy8qifNbs2t+khDH26RPhamv5KEUhEKg5tkirTcwlBoqioPg9BKudhFwV9MmuwGJSUVl9/OBzOn04nUev4nS5AjgcXgoLax+J6/VqRo2q/4IjUX8SfEIkgLDfTygpHUWlRqXU/pyo6vPPOZiaSsbNN5GTlYVv50723XgTwToCU6VSo+/UMRplA7B1q53XX9/A11/vY8eOcgKBMIqikJys56STshg3rhu//30/MjLic8uDooQJuasIuZwEqysIVVeACrSWVDTWVE6/cAi+u3ZE/HP1eg1nnNFy9z7GknRuESJOKYpC5dpCimYUcGD2fCzdOpDm2ALBpjeLPkKnI33S1eTcc0/k7vmz7dsdTJ36OevXlxEIhGpdAGI2awmHFSZP7sPf/34mSUnHNs5uTkooWBNwh4POXYlab0RrTUVjSUVrTUWlNx7VT/Wii+bw+ed7IvqotFOnZHbvvl4aVUeBBJ8QccZ74CDF739G0fQCQm4PeZPyyb16HMZOuey7/wG8CxZAoO7l8/WlMhrpOn8eutzciNzvsH/+cw33378Eny9EOFy/rxijUUNysoGCgnyGD2+evWs105Zegq4KQtVOgq4Kwj4PGnPSkRGd1pKCSlt3l5rvvz/A2Wd/0OQm3odZLDpeeOFMrr32pIjcTxxNgk+IOBDyeCmdu4ji6Z9QvmojbfLPIe+a8aSfNhhUKqqqKqlwODAqUDHxakLl5U3+TJXJRMaNN5J1040R+BX84v77F/PCC40/ycFs1jJv3qWceWaHiNYFNdOWYU/1z1OWNUGHoqCxphwJOo0pCVUDjog6bNq0L3j//a0RaVLdko9uigcSfELEiKIolC9bQ9H0Ako/+ZKUIf3Jm5xPm3FnozHXPO/yejzYbTZUahUZmVkYDAZcK1aw/6ab6+zZeSIqvR5Dz550en8mKm3kHvW//fZmbrttUZNHPlarjg0bptClS2qT7qOEggRdTkLVFTX/7XKi1ht/nrJMQWNNRa03RWQ6sarKz8knv8P+/VUN3q/3a6mpBtaundwij26KFxJ8QjQz9579FM2YS/GMAtQGPXmT88mdOA5jbs6Ra4LBAA67Ha/XS3p6Bhar9agv58r/fcmBe+5pVPipDAb0XTrTcfp0NNbILZcvKqqiV6+3TtjFpD40GhUDB2azcuWkBo16wn7vz8/nKghW/3baMgWNJRX1CaYtm6KkpJoRI2ZSUuI6YQuz3zp8EO13313JSSdlRalCARJ8QjSLQGU1pXO+oGh6AdVbd9H2iovIm5xPyuB+RwVaOBzGWVFBpbOC5JQUUlLTUNcy7eZZv56iP95JqKKi3gGoMhpJyc8n5567UZsiu4ryqqvm8dFH22tdxNKmjYWnnz6DCy/sTFKSnt27ndx880IWLy467vVWq47//vcCLrusx3F/rihKzbSlq2alZbC6ApTwkQUoGksKGnNyo6Ytm8Lp9HHzzQuZO3dXvUe+FouOAQOymTnzIjp0SI5yhUKCT4goUUIhbF9/T9G7n3Do8+/IGD2MvEn5ZF84CrX+6JWLiqLgdrlw2G3oDQbSMzLR6U48Mgl7vTjeehvHu++iBAKEfb5jVn2qTCYIhTANOJmsP/4R86BBEf11AjgcHnJz/1Xr862UlJrpu6VLi3n55XWUlXno0iWFAweq2bat9lZsQ4e2YdWqSUDNtGXIVflL0LmcqHWGmgUoPwed2mCOm1WQX321l0ceWc4PP5Si1aqprj56JGw210wxd+qUwoMPnsqVV/aKm9pbOgk+ISKsassuiqZ/QvHMTzG2ySJ38nhyr7wIfebxu6P4fT7sNhuhcIiMzExMJnODP1MJBnGvWoV7/QY8a9cQclZSVbiT5JGnknruWZhPHY4+L7IrN3/tP//ZxB13fF3rNOeTT45k1Kj2jBz5foPuazCo2bL0LLKtXkJeFxpT0s8rLWumLtXa2G59qI+ffnKyZEkRy5YdYM+eCsJhhXbtrJx2Wi4jRrSjXz+Z1mxuEnxCRIDfXs6BWfMpmv4J3pJD5E68hLxJ+ST1rf1w11AoRLnDjsvlIi0tnaTk5Ij+jX/lhdfS+Q/XkH3+qIjdszZTpizg3Xe31PrzwsKpfPHFHnJzrZx5ZgcOHKjmzTc38cor6+q8b7JVw9uvDOOSS/ugMSehUreukx9EdEjnFiEaKez3c+jzxRRN/wT7NyvIvnA0PR/7I5lnjUClqf0LWlEUqiqdlDvKsVqt5LXvgKaO6xtLn5mG39b0bQ/1sWbNwTp/3qVLCrfcMoDnn1/D009/xIAB2bz00hiAOsPP5Qmzdb+aS62pkSxXtHISfEI0gKIoONdspnjGXA58MB9rzy7kTs7n5Lf+hi75xCskPW43dpsNjVZD23bt0BsMUavVkJWOv6x5gu+3z69+S61W8cMPpdx//xIA1q8/RPfuqdx664A6gy8UUo7pcylEU0nwCVEP3gMHKZ75KUXTCwh7feROyue0pR9g7lK/XoqBQACH3Ybf5yM9IxOzxRL1hQz6rHT8tuic4fdbOl3dKydLSlxs2XL0Ablbtzq4444Tr2A0GGR6U0SWBJ8QtQi5PTXdVGYUULF6E23yz6H/K4+SdtrgeodWOBymorycqkonKampZGXn1Lo9IdL0mem4dzfPCd49e6aza1dFrT9ftqyYnj2PXtzTo0cae/dW1nlfq1V3zPuEaCoJPiF+5Ug3lXc/obRgIalD+5M7eTyDP3z5SDeV+t7HVV2Nw27HaDKS274D2gh2SKmP5hzxjR7dnoULf8LvP37HkuefX8Py5Vdx//3DmD17OwMHZvOHPww6MvVZG0WBwYNz6rxGiIaS4BMCcO/eT9GMAopmFKAxGcmbnM8Z6z/D2K7hX7o+nxe7zYaiKGTn5GCM8Ebx+tJnpuFrpmd8Y8d24S9/WQYcP/h++KGU/Py5PPXUSB56aDj79lXy0ENLefXV9XXeNylJLyM+EXESfKLVCjirKJnzBcXTC6jevpt2V1zE4PdfIHlQ30Y9fwsFgzgcDjxuF2npGViTkmK6Ibk5R3y9emXQr18mq1eX1nrNggW7WbBgd73vaTJp+dOfhkijZhFxEnyiVVFCIWxfLadoekFNN5UzT6XzH6eSfcEZx3RTqfc9FYVKZwUV5eVYk5LJa98BdRS2JzSUPjMNf1nzBB/Ac8+N4oIL5kTsaB6TScv118uxPCLyJPhEq1BVuJOiGQU13VTaZpM3eTx9n3+g1m4q9XW4zZhWp6Ntbh76RoZnNOhSkwm5PIT9/kaHekOccUZ7rr66N9Onb2ny0Txms5YZMy4kJSV62z1E6yXBJ1osv81B8az5FE//BG9pGXlXj2PY52+T1Kdbk+8d8Pux220EAgEyft6eEG9UajX6jFT8tvJGPatsjBdeGMO6dYfYvMmGt4GnExxmNmu5887BXHBBlwhXJ0QNCT7RooT9fg4t+I6iGQXYv11Z003libvIHDO8zm4q9b5/OEyFw0FVVSWpaWnktGkb142F9Zk1m9ibK/hMJh3ffDOBUV2fZatDjyfYsH82JpOWe+45hYceGh6lCoWQ4BMtwOFuKkXTCyj5YD7W3t3Ia0A3lfp+RnVVFeUOOyazOSbbExpDn5XWbAtcDit7exbPt/+Bjfffxz33L0dRwOOp+7lfUpKOlBQDH3xwCcOHt2umSkVrFf9/coWohbf4IMUz51I0Yy5hr4+8yfmctuzDendTqffneH8+BR3IadMWg9EY0ftHkz4rHV8zLnBxLFvDrqf/xWlLZnNGl/ZMnNyft97azIsvrqW01IXZrONwX3xFAa83yIAB2dx991DGjeuGThf7RUGi5ZPTGURCCbk9lBYspGjGXJw/bKLN+HPJm5zfoG4q9RUMBmtOQfe4Sc/IPOYU9ESw+Q+PYeneic63XxP1z/IdtLF02KX0e/Uxci4cfczPnU4fGzYcwm73otGoyMtLol+/TPR6CTvRvGTEJ+KeEg7j+FU3lbRhJ9N+yqUMmfMKGlPkR1/hcJhKZwXOigqSklPI69Cx2dqMRVpz7eULB4OsvfpO8qZcetzQg5rDaM84I7KjcSEaQ4JPxC3Xj/sofm9uTTcVs4m8yfmM2jAvags1FEXB7XbhsNnQ6w20y2tfr1PQ45k+M42qTduj/jnbH3oetV5Pj7/cHvXPEqKpJPhEXAk4qyj56AuKpn+Ca8ce2k24iMGzXiR5YJ+oTjP6/X7stjJCwSCZWdmYzA0/BT0eGbLSsUf5TL7SgoWUfLCAkSvnRGTlrBDRJsEnYk4JhShbtIzi6QUc+mIxmWOG0+WuaWSf3/huKvUVCoWoKHdQXVVFalo6ySkpCfccry76rPSodm9x7fyJTbf8haFzX29yMwAhmosEn4iZqs07fummktumppvKCw+hz0iL+mfXnIJeSXm5A4vFQl6HjlE5BT3W9Jnp+KL0jC/ocrPm8tvp8fAfSB0qrcVE4pDgE83KV+bgwKx5FE0vwH/IRu7V4xj2v3dI6t212WrweDw4bGWo1WratG2HIYqnoMdatEZ8iqKw6ea/kDywDx1uuDLi9xcimiT4RNSF/X4Ozv+W4umfYF+8muwLR9PrqT+ReeapzfpMKBAIUG634/V5Sc/IwGJJvO0JDaXPSCVYUYUSCkX0n/Xef82kavMOTls6u8X/MxQtjwSfiApFUXD+sKmmm8qHC7D26U7e5HwGvPMs2qTIdFOpr3A4jLOinEqnk+SUFDKzsxN2e0JDqTQatKlJ+B1ODFmReQZXvmI9Ox9/mRGLZzXocF4h4oUEn4goT1EpxTM/pXhGAWF/gLxJ4zht+YeYOzf//i1FUXC5fj4F3WAkN6892gTfntAYh6c7IxF8vjIHayf+kZNefwJLt44RqE6I5ifBJ5rsSDeV6QU412ymzaXn0f+1x0kbMShm02A+nw+HrYxwOExWdg6mGJ2CHg8MmZHZxK6EQqybdBe5Ey8h5+KzIlCZELEhwScaRQmHcSz9oaabytxFpJ06gPZTf8eQj1+NSjeV+gqFQpQ77LhcLtLS0klKTm71z6AitcBl+yMvAtDz0TuafC8hYkmCTzSIa9feI91UtBYLudfkM+rxOzG2zY5pXTWnoDupKHdgTUoir32HFrk9oTEicRL7wc++ovi9uYxcIZvUReKT4BMnFHBWUfLh5zXdVHbtremm8sHLJA/oHRejKY/bjd1mQ6PV0DY3F72+5W5PaIym9ut07drLxhsfZMjHr2HIzohgZULEhgSfOK5wMIht0TKKphdQ9sViMs8aQdc/X0fW+WegjpMFIoFAAIfNht/vIz0zE7PZEhdBHG/0GWm4ftzXqPeG3B7WXHE73R+8jbRTB0S2MCFiRIJPHKVy03aKZxRQ/P5nmPLakjs5n34v/qVZuqnUVzgcpqK8nKpKJympqWTl5LSa7QmNoc9Kp3zl+ga/T1EUNt/2KEn9etDx5omRL0yIGJHgEzXdVN7/rKabis1B7tXjOPXLd7D2ar5uKvWhKArV1VWU2+0YTYlzCnqsNXZxy75/z8a5tpARy2STumhZ5FujlQr5/Bxa8Es3lZyLzqT30/9Hxuhhcbl4wef1YreVoQDZbdpgNLbe7QkNpc9sePBVrN7IjkdeYPi3M9FaWsZJFUIcJsHXiiiKgnP1Joqmf8KBDxeQ1K8HeZPHx6SbSn0Fg0HKHXY8bjdp6RlYk5Jk9NFAhqx0/A04mshvc7D2yjvo/+pjWHt0jmJlQsSGBF8r4CkqPbIFQQmGyJs0jpEr5mDulBfr0mqlKArOigqcFeUkJSUn9CnosabLTMNvK0dRlBP+pUEJhVh3zf/R9vILaJN/TjNVKETzkuBroYIuN6UFCymeXoBz3RbaXnoeJ73+JGnDB8b9iMntcmG329DpdLTLzUMX5TP5WjqNQY/GbCRYUYkuLaXOa3c8/jJhv5+eT9zVTNUJ0fxUiqIosS5CRIYSDuNYspqidwso/XQRacMHkjc5n5yxY2LaTaW+/H4/DruNYCBAekYmZosl1iUltL17nXz00Q6+/XY/q78oJJySisGkp1evNEaNas/48d3p2zfzyPUHF3zL5lsfZuSKORhyMuu4sxCJTYKvBXDt2ltzoOt7c9FaLeRdM552V12MsU1WrEurl3AoRHl5OdVVlaSmpZGckhr3o9J4Vlho4/bbv+L77w+gKODzhY65RqdTo9Wq6d07nX/+cwyD8xSWjZzA4A9eIv20wTGoWojmI8GXoAIVlb90U/lxH7lXjiV38niST+6VMKGhKArVVVWUO+yYzBbS09PRyPaERguHFZ56agVPPbUSrzdIff9km0xazrXs5+n7+tPrrt9HtUYh4oEEXwIJB4PYFi6jaEYBZf9bQuZZI8ibnE/WeafHTTeV+vJ6PNjtNlQqFRkZmRiM8T8VG8/CYYVJk+Yzd+4u3O5gg99v0IQZPCyPRYsux2RKrN9LQjSUBF8CqNy0naLpBRx4/zNMHdqRNzmftldciD49NdalNVgwGMRht+H1eEjPyMRibfmnoDeHu+76htdf39Co0DvMZNIyenR75s+/VP6diBZNgi9O+Q7Zf+mm4qgg9+px5F19Sdx1U6mvcDhMpbMCZ0UFSckppKalyfaECFm2rJhzzvkQj6fxoXeYxaLjtdfOZvLkvhGoTIj4JMEXR0I+P4fmf0PR9E9wLPmBnLFjyLtmfE03lQQNCUVRcLtcOOw29AYD6RmZ6BJsWjaeKYpC165vsmeP87g/f/jhETzyyIijXistddG27Wu13jMpSUdJyc1YLLKNRLRMspIgxhRFoWLVRoqmf0LJR5+T3L8neZPHM/Dd5+K2m0p9+X0+7HYboWCIzKxsTGZpfRVp3323n7Iyd53XbNtmZ/To2Uf+fyhU9991FQVmzdrOtdf2j0iNQsQbCb4Y8ewvoXjmpxRN/wTCCrmTxjFy5ceYO+bGurQmC4VCVDgcVFdXk5aeRlJyijwzipJXX12PyxWo85pgUOHgwbrD8deqqwO88MIaCT7RYknwNaOgy03pJ19SNL2AyvVbaXvZeZz877+SeuqAFhEMiqJQVVlJucOBxWohr4Ocgh5ty5YVn3DbQpcuKRQX34TPF2LlyhLuv39JrVOjh23b5iAQCKHTyb8/0fLIM74oU8Jh7ItXUfxuAaWffUX6iEHkThpHzsVnoTG2nJPCPR43DpsNtVpNemYWBkPL+bXFK7c7QErKSwSD4VqvOf/8ziQl6dm2zU52tpkHHxxOr17p9O37Ng6Ht9b3JSXpWLZsIv37J0YTBCEaQkZ8UeLa+dPP3VQ+RZtsJW/yeHo+9aeE6aZSX4FAAIfdht/nO9JmrCWMXhNBRYUPnU5dZ/B98cWeo/7/ihUl7N59PVOm9OX559fU+j61Wk15ee3BKEQik+CLoEBFJQc+WEDR9AI8e/bT7sqxDJnzKskn94p1aREXDodxVpRT6XSSnJJKVracgt7cNBoVDZ2wcbkCFBba6N497QRXKmg08u9TtEwSfE10pJvK9E8o+3IpmWePoNu9N5J17siE66ZSH4qi4KquxuGwYzQayc1rj7YF/joTQWamiXC4YcFnMGjo1Sudb77ZX+d1Pl+Ijh2Tm1KeEHFLgq+RKjduq+mmMmsepo655E3Op/8rj57w2JdE5vP5ak5BD4fJzs7BaJJT0GNJo1HTvXsahYX2Wq959tlRfPbZj+zbV0V2tomHHhqOxaLjnXcK67y3TqchNzext9MIURsJvgbwHbRR/P48imf80k3l1EXvYu3ZJdalRVUoFKLcYcftcpGank5SUrI8x4sT48Z1Y9euiuOewACQl5fE+++PJTPTRFmZmxUrSjj11Jns21dZ6z1VKhgzpoP8OxYtVtyv6qyq8vPpp7tYurSYlStLqKryo9Op6dEjnVGj8rjggs706pURtc8P+fwcmvc1RdMLcCz9gZyLzyLvmnwyRiVuN5X6UhSFSqeTinIH1qQkUtPSZXtCnCkqqqJbtzdrDb7GsFp1fP75ZYwcmRexewoRT+I2+MrK3Nx33xJmztyKVquiqurYTboGgwaVSkX//pk8++woRo1qH5HPVhSFipUbarqpzPmC5JN6kTd5PG3Gn4PW2joOR3W73ThsZWi1WtIzs9DLKehxa8KEz5g7d1dEwk+tVnHyyVmsWTNZRnyixYrL4JszZwfTpn2B1xvE7699qfavmUxaJk7szYsvjsFsbtxiC8++A790U1Egd3I+eVePw9ShXaPul4gCAT8Omw2/P0B6ZiZms1m+AOOcw+GhW7c3KS/3NfleJpOW9euvoUeP9AhUJkR8irvge/bZVTzyyPJGHa9iNGrp3Tud7767kqSk+o1QgtUuSj9ZSNH0T6jcsI22vzufvMnjSR12cqv6wg+Hw1SUO6iqrCQlNY2UVDkFPZF8++0+Lrro4yYdS2Q2a3nllbP5/e/7RbAyIeJPXAXfO+9s5pZbFjXpD6/BoGHw4ByWLLkKtfr4X9xKOIz9u5UUvVvAwXlfk37aYPImjSN77JgW1U2lPo46Bd1kJi0jA62cgp6Q/ve/PVx22ad4PMEGb3MwmbT8859ncsMNJ0epOiHiR9wE3759lfTp8/YJG+7Wh8Wi4/HHT+POO4cc9Xr1jj0U/9xNRZeaTO7k8eReNRZDTmaTPzMReb1eHLYyFCAjMwujnIKe8HbtKmfChM/YsaOc6uoT/1myWHRkZpr48MOLGTq0bTNUKETsxU3wnX/+R3z11b7jtl+6995TuPTSHvTsmYbPF2LFihLuu28JhYW2Wu9nNmvZvft60vXBmm4qM+bWdFO56mLyJuW3yG4q9RUMBil32PG43aSlZ2BNSpJpzRYkHFYoKNjJM8+sZt26gxiNWrzeEIFACK1Wjcmkxe+v2aB+773DuOqqXhgMMsoXrUdcBN/evU569XoLr/f4q9K++OIyZs3azurVpahU8NhjpzF8eDv69Hm71n6CBr2KqT3KyS/5gqxzTiN38viabiqteBpPURScFRU4K8pJSk4mNS1d2oy1cDabmzVrDlJYaMflChzp3DJ4cA65uUmxLk+ImIiL4Hv44WU8/fQq/P76Lce2WHQ4nbeTn1/AvHm7a70uM0nNgb3XtuhuKvWhKAoetxu73YZOpyMjIxOdbE8QQrRScTH8Wbhwb71DDyApSY9Goz7h8u0qv4rygJbsphaYwPz+mu0JwWCAjMxMzObWsQ9RCCFqExfzXHU9qzueF14Yw7p1B/n++wN1Xmc0ali79lBTSktYoVAIu62MkuIiTGYzue07SOgJIQRxMuKrz+qzw/7+99GMHJnLyJHvn3DJdiikYLd7mlpeQlEUhaqqSiocDkxmC3ntO6Bpxc81hRDit+LiG7G+Cwr/8Y/RXHllL8488wP27HHW476qWvfytURejwe7zYZKrSKnbVsMBtmeIIQQvxUXwZeRYeLQIXed1/zzn2cyYUIvzjxzNtu3O+p1X5Wqpjt9SxcMBnDY7Xg9XtIzMrBYrbI9QQghahEXz/gGDap7+cnLL5/F1Kn9mDhxHuXlXnJyzOTkmLFY6u7J6fEEGTiw5S5tCYfDlDscFO/fj06nI69DB9mTJ4QQJxAX2xlefXUdd9+9uNauLYry5+O+/sgjy3n00eW13rd373S2bJkWkRrjiaIouF0uHHYbeoOB9IxMdHIKuhBC1EtcBF9lpY82bV7D42l8j87fslp1vPTSWS2u4a7f58NusxEKh8jIyMRkNse6JCGESChxMdWZnGzguuv6YzJF7pGj0ahlwoSeEbtfrIVCIWxlZZQcOIDFaiE3r72EnhBCNEJcjPgA3O4A3bq9SUmJq8n3Mpu1FBTkc845nZpeWIwpikJVpZNyRzkWq5W0dDkFXQghmiIuRnwAZrOOuXPzT7hg5cT30XLDDSe1iNDzuN0UF+3HVe2ibbt2ZGZlSegJIUQTxc2I77AlS4q48MI5eDxBQqGGlWY2a/n97/vx8stnJfTKxkAggMNuw+/zkZ6RidliSehfjxBCxJO4Cz6A3bsruPLKeWzZYq/X+XxGowa9XsObb57H5Zcn7nO9cDiMs6KcSqeT5NRUUlJS5fQEIYSIsLgMPqg5U2zOnB387W+rKCy0oddrqK4OHGlTpjfoMOhV6HRqbr99ILfdNpDMzMRc7KEoCq7qahx2O0aTkfSMDLRa2Z4ghBDRELfB92tFRVWsXl3Khg2HsNu9GI0a9rlS+cPkNgwbmo1Wm7ijIp/Pi91mQwkrZGRmYjSZYl2SEEK0aAkRfMcz/asQp/dT0yknMZ99hYJBHA4HHreLtPR0rEnJ8hxPCCGaQcIOlTKSVZQ5Ey+za05BL6do/z7UahW57TuQlJwioSeEEM0kLppUN0ZWCtgqY11FwxxuM6bV6mibm4deTkEXQohml7DBl5msYntRONZl1EvA78dutxEIBI60GZMRnhBCxEbiBl8K2E58JF9MhcNhKsodVFVWkpKaRk6bthJ4QggRYwkbfFYjBMPg9imYDfEVJoqiUF1VRbnDjslsJrd9B7RyCroQQsSFhP02VqlUZCbXjPo6xNGRe16vF7utDBWQ06YtBqOcgi6EEPEkYYMPIDNFha1SoUN27Ed8wWCQcrsdj8dNWkYGVqscCCuEEPEosYMvOfYrO8PhMJVOJ86KcpKSk8nr0FHajAkhRBxL8OBTsbskNis7FUXB7XbhsNnQ6w20y8tDp5PtCUIIEe8SOviyUqAsBiM+v9+P3VZGKBgkIysLs9nS/EUIIYRolIQOvmQz+ALg9SsY9dF/nhYKhagod1BdVUVqWjrJKdJxRQghEk1CB9+RlZ2VkJcZvc+pOQW9kvJyBxaLhbwOHeVAWCGESFAJHXxQ85zPVqmQlxmdkZfH48FhK0OlVtOmbTsMBkNUPkcIIUTzSPzgi1IHl2AggMNux+vzkp6RgcVilWlNIYRoARL2WKJAIMT8+bv5sGAvS5cU4XO5URTIyDAyfHg7zj67I/n53TCZGnaga80p6BVUOitITkkhJTVNticIIUQLknDBFwiEePbZ1Tz77GpCIYWqKv9xr0tK0qEocNNNJ/Poo6dhNtcdgIqiHDk9wWAwkpaRgU4np6ALIURLk1DBt2WLjUsvncv+/VW43cF6vcdk0pKWZuCjj8YxfHi7417j8/lw2MoIh8OkZ2ZiMpkjWbYQQog4kjDBt2pVCWef/QHV1QEaU7HJpOWjjy7hwgu7HHktFApR7rDjqq45BT0pWU5BF0KIli4hgm/vXicnnfQOlZXHn9asL7NZy5IlVzFwYDaVlU4qHOVYrVZS09Nle4IQQrQScR98iqIwYsRMVq8uJRRqeqmdOiWx6MtzMVv0ZGRkopftCUII0arE/XLFDz7YzqZNtlpD7/TT85g7N5+iohtRlD8zZUrfOu9XWupm5vtFtGnbTkJPCCFaobgPvqeeWonLFaj151arjs2bbdxxxze43bVfd5jXG+LlVzYRDsf1QFcIIUSUxHXw7dxZzs6d5XVe8/nne3jggaXMmbOj3mHm8QRZvLgoEiUKIYRIMHEdfCtXlqDRRH6Vpdcb5PvvD0T8vkIIIeJfXAffqlUlVFefePqyoQKBMEuXFkf8vkIIIeJfXAffwYOuqN3bZvNE7d5CCCHiV1wHn1YbvfK0WtmoLoQQrVFcB1/PnulRC6gePdKicl8hhBDxLa6PJRo6tA1ms67Oji0Wi45u3VIBUKtVdOiQzMknZ+FweNm/v6qW92g57bS8aJQshBAizsV15xan00ebNq/h9dbekHrUqPZ8++2EY17/7383M3XqF8d9j9GoYfPmqXTtmhqpUoUQQiSIuA4+gEmT5jNr1raItCs7bMSIdixbNjFi9xNCCJE44voZH8ADD5yKXh+5BtImk5YnnxwZsfsJIYRILHEffL17Z3DPPadgNjf9caTRqGHChJ6MHt0hApUJIYRIRHE/1Qk1p66fffaHrFpVgtcbatQ99Ho1XbumsnLlJJKS9BGuUAghRKKI+xEfgE6n4YsvLmP48HZYLLoGv99s1tKjRzpLl14loSeEEK1cQgQfgMmkY9GiK3jssRGYzdp6PffTalWYTFruuGMQa9ZMJj3d1AyVCiGEiGcJMdX5W/v3V/Lii2t5/fWNHC7f4wmiKDXP8TQaNaFQmEmT+nDXXUPo2TM9xhULIYSIFwkZfIcFg2G2bbOzZs1BSkpchMMK2dlmBg3KoV+/zIiuBhVCCNEyJHTwCSGEEA2VMM/4hBBCiEiQ4BNCCNGqSPAJIYRoVST4hBBCtCoSfEIIIVoVCT4hhBCtigSfEEKIVkWCTwghRKsiwSeEEKJVkeATQgjRqkjwCSGEaFUk+IQQQrQqEnxCCCFaFQk+IYQQrYoEnxBCiFZFgk8IIUSrIsEnhBCiVZHgE0II0apI8AkhhGhV/h8q8Je/tkKSBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution15_vqe =  sol15_qasm_vqe_cobyla[0].variables_dict\n",
    "G_sol15_vqe = nx.Graph()\n",
    "G_sol15_vqe.add_nodes_from(range(n_companies))\n",
    "for k, v in solution15_vqe.items():\n",
    "    if v == 1:\n",
    "        nodes = k[2:].split(\"_\")\n",
    "        G_sol15_vqe.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "for k, v in solution_not_kept.items():\n",
    "    if v == 1:\n",
    "        nodes = k[2:].split(\"_\")\n",
    "        G_sol15_vqe.add_edge(int(nodes[0]), int(nodes[1]))\n",
    "nx.draw(G_sol15_vqe, pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "da63ac07",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol15_qasm = {\"qaoa_cobyla\":sol15_qasm_qaoa_cobyla, \"vqe_cobyla\":sol15_qasm_vqe_cobyla}\n",
    "np.save(\"./Data/sol15_qasm.npy\", sol15_qasm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "21d19048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution15 = [G_sol15_qaoa, G_sol15_vqe]\n",
    "fig, ax = plt.subplots(2,2, figsize=(15,10))\n",
    "ax[0,0].plot(sol15_qasm_qaoa_cobyla[1][\"mean\"], color=\"darkblue\", label=\"QAOA-COBYLA\")\n",
    "ax[0,1].plot(sol15_qasm_vqe_cobyla[1][\"mean\"], color=\"slateblue\", label=\"VQE-COBYLA\")\n",
    "for i in range(2):\n",
    "    ax[0,i].grid()\n",
    "    ax[0,i].set_xlabel(\"Iterations\", fontsize=14)\n",
    "    ax[0,i].set_ylabel(\"Cost\", fontsize=14)\n",
    "    ax[0,i].legend()\n",
    "    plt.sca(ax[1,i])\n",
    "    nx.draw(solution15[i], pos=companies, with_labels=True, node_size=500,\n",
    "        edge_color=colors, width=1, font_color=\"white\",font_size=14,\n",
    "        node_color = [\"tab:red\"] + (n_companies-1)*[\"darkblue\"])\n",
    "fig.savefig(\"./Images/Sol15Q.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251091df",
   "metadata": {},
   "source": [
    "## Solution with 20 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fea00687",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_1s = 8\n",
    "num_0s = 12\n",
    "random.seed(1)\n",
    "keep_1s = random.sample(index_1s, num_1s)\n",
    "keep_0s = random.sample(index_0s, num_0s)\n",
    "sol_qubo = sol.variables_dict\n",
    "solution_not_kept = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s} \n",
    "\n",
    "qubo_20vars = qubo_no_slack.substitute_variables(solution_not_kept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c332f4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol20_qasm_qaoa_cobyla_r1 = Optimization_QAOA(qubo_20vars, reps=1, local=False, backend=provider2.backend.ibmq_qasm_simulator,\n",
    "                                       provider=provider2, optimizer=COBYLA(maxiter=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "262621a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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