lab_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dfb2ccb8",
   "metadata": {},
   "source": [
    "# IBM Quantum Challenge: Spring 2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5055920",
   "metadata": {},
   "source": [
    "## First Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7408b2",
   "metadata": {},
   "source": [
    "Welcome to the IBM Quantum Challenge: Spring 2023!\n",
    "\n",
    "This iteration of the IBM Quantum Challenge is dedicated to dynamic circuits. You're currently in the first lab of the challenge. As work through the labs, you will be required to complete some exercises. In this introductory section, we will give you a brief overview of how to complete the exercises. Even if you have participated in previous challenges, you may want to review this material.\n",
    "\n",
    "All the labs will contain a mixture of tutorial content, pre-written code blocks, and exercise code blocks which will require you to fill in your own Qiskit code. The code required to complete the exercise should be typed under the line which has the _\"### Your code goes here ###\"_ comment. \n",
    "\n",
    "If you've never been part of a Challenge before, it's important to note that you should run every code cell, even if you didn't write any new code within. This makes sure that when you submit your answers by running the grader, everything is up to date. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e89268",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Sample Exercise\n",
    "\n",
    "In the cell below, we construct a circuit with a single qubit and a single classical bit. Your first task is to perform a Hadamard gate on the qubit and then measure it, storing the result in the classical bit. We've filled in the code for you, all you need to do is remove the `#` at the start of both lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3195c89",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 287.093x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "\n",
    "qr = QuantumRegister(1)\n",
    "cr = ClassicalRegister(1)\n",
    "qc = QuantumCircuit(qr, cr)\n",
    "\n",
    "\n",
    "\n",
    "qc.h(qr[0])\n",
    "qc.measure(qr[0], cr[0])\n",
    "\n",
    "\n",
    "qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e56990",
   "metadata": {},
   "source": [
    "Nice job! After each exercise we'll need to figure out if what you wrote is correct. To do this, you simply run a grader cell.\n",
    "\n",
    "Let's try it out. In the next cell, we ask you to set an integer value for the variable `answer_0`. If the integer is between 1 and 5, the grader will give back a message congratulating you for finding the right answer. If you enter any other number, you will get the opposite message. \n",
    "\n",
    "Set the answer to whatever you'd like, then run the grader cell to see your results. You can try this a few times with different numbers for fun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5100dd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "answer_0: int\n",
    "answer_0 = 5\n",
    "\n",
    "##### Hint: Remember to run this cell after changing your value ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35f09765",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Grader Cell: Run this to submit your answer\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex1a\n",
    "\n",
    "grade_ex1a(answer_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95432dc",
   "metadata": {},
   "source": [
    "Now you're all set to start this challenge. Good luck and have fun!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "312c9431-a3fc-4448-9001-fb9d41ea6a44",
   "metadata": {},
   "source": [
    "# Introduction to Dynamic Circuits and Repeat until success"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7212735",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fb8748",
   "metadata": {},
   "source": [
    "According to the [Qiskit textbook,](https://learn.qiskit.org/course/ch-algorithms/quantum-circuits) \n",
    ">_\"A quantum circuit is a computational routine consisting of coherent quantum operations on qubits ... It is an ordered sequence of quantum gates, measurements and resets, all of which may be conditioned on and use data from the real-time classical computation.\"_\n",
    "\n",
    "You might not be familiar with the last part of the definition, but it has everything to do with dynamic circuits. Usually when we talk about quantum circuits, we're referring to _static_ quantum circuits, even if we don't explicitly say it. So what are dynamic circuits?\n",
    "\n",
    "**Dynamic circuits** are quantum circuits that contain mid-circuit measurements where the results of those measurements are used to condition quantum gates later in the circuit. The ability to condition future quantum operations on the classical measurement results is known as classical feedforward.\n",
    "\n",
    "Why do we need classical feedforward? An example may help: Consider the conditional reset operation, which may sound familiar if you've ever used the [`reset`](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.reset.html) operation in Qiskit. While the reset operation is a non-unitary operation that resets the qubit to the zero state no matter the initial state, the conditional reset operation is performed on an already measured qubit to reset it to zero by applying a bit-flip X gate only if the qubit is in the one state, as determined from its measured value. The conditional reset can be a faster and less error-prone way to reset a qubit if it has already been measured."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ea3680",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"resources/sketch-1.png\" width=\"50%\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caea194a",
   "metadata": {},
   "source": [
    "Put another way, dynamic circuits are quantum circuits that include control flow such as if statements and while loops, where the predicates are computed from the results of previous qubit measurements and the conditional operations include quantum gates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52cef83-0393-4aba-9df0-ff54dff0bc04",
   "metadata": {},
   "source": [
    "## Hello Dynamic Circuit World"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1291e604",
   "metadata": {},
   "source": [
    "IBM Quantum hardware has already supported mid-circuit measurements and reset for some time. However, adding support for classical feedforward required a significant redesign of the control-systems and the associated software stack. Full support for dynamic circuits was introduced only last year (2022).\n",
    "\n",
    "In Qiskit, the syntax for programming dynamic circuits has gone through several iterations, and full support has not yet been implemented. Currently, the only way to access the full capabilities of dynamic circuits is to submit programs written in OpenQASM 3. Nevertheless, in this challenge we will be working with what is currently available in Qiskit.\n",
    "\n",
    "An earlier version of Qiskit introduced the [`c_if()`](https://qiskit.org/documentation/stubs/qiskit.circuit.Instruction.c_if.html) instruction, but this syntax will be deprecated in favor of the more flexible [`if_test()`](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.if_test.html) method of QuantumCircuit, which is the method we'll be mostly using in this challenge.\n",
    "\n",
    "To get you started, we're going to review a simple example where we'll use this function. We'll build a circuit that demonstrates the conditional reset operation by taking the following steps:\n",
    "\n",
    "1. Initialize a circuit with 1 qubit and 2 classical bits. ($q_{0}$, $b_{0}$  and $b_{1}$)\n",
    "2. Apply a Hadamard gate to $q_{0}$.\n",
    "3. Measure that qubit and save the result in $b_{0}$.\n",
    "4. Begin an `if_test` block conditioned on $b_{0}$ being equal to 1.\n",
    "5. In the `if_test` block, specify the operation to do if the condition is met, in this case, flipping $q_{0}$ to 0 state.\n",
    "6. Measure $q_0$ again into $b_{1}$ to check that we always get 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10d6953d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 553.955x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import QuantumRegister, ClassicalRegister\n",
    "\n",
    "qr = QuantumRegister(1)\n",
    "cr = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(qr, cr)\n",
    "\n",
    "# unpack the qubit and classical bits from the registers\n",
    "(q0,) = qr\n",
    "b0, b1 = cr\n",
    "\n",
    "# apply Hadamard\n",
    "qc.h(q0)\n",
    "# measure\n",
    "qc.measure(q0, b0)\n",
    "\n",
    "# begin if test block. the contents of the block are executed if b0 == 1\n",
    "with qc.if_test((b0, 1)):\n",
    "    # if the condition is satisfied (b0 == 1), then flip the bit back to 0\n",
    "    qc.x(q0)\n",
    "# finally, measure q0 again\n",
    "qc.measure(q0, b1)\n",
    "\n",
    "qc.draw(output=\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3da6a2",
   "metadata": {},
   "source": [
    "Now that our circuit is built, let's run it several times to see if we always get the expected output. The first measurement could be either 0 or 1, but the second measurement should always be 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06a4eee8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts: {'00': 515, '01': 509}\n"
     ]
    }
   ],
   "source": [
    "from qiskit_aer import AerSimulator\n",
    "\n",
    "# initialize the simulator\n",
    "backend_sim = AerSimulator()\n",
    "\n",
    "# run the circuit\n",
    "reset_sim_job = backend_sim.run(qc)\n",
    "# get the results\n",
    "reset_sim_result = reset_sim_job.result()\n",
    "# retrieve the bitstring counts\n",
    "reset_sim_counts = reset_sim_result.get_counts()\n",
    "\n",
    "print(f\"Counts: {reset_sim_counts}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388ce901",
   "metadata": {},
   "source": [
    "As expected, the first bit is sometimes 0 and sometimes 1, but the second bit is always 0 (recall that Qiskit uses little-endian bit-ordering, so that the right-most bit is the first bit and the left-most bit is the last bit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "439359c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import *\n",
    "\n",
    "# plot histogram\n",
    "plot_histogram(reset_sim_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de501c69",
   "metadata": {},
   "source": [
    "Now you're ready to build your first dynamic circuit!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62264b8",
   "metadata": {},
   "source": [
    "### **Exercise 1**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da73cc7b",
   "metadata": {},
   "source": [
    "Let's level things up. Your first assignment will be to design a two qubit circuit. In this case, the aim will be to act differently on $q_{1}$ depending on the value of $q_{0}$. If the measurement on $q_{0}$ reads 0, apply an $X$ gate on $q_{1}$. If it reads 1, apply a Hadamard on $q_{1}$ instead.\n",
    "\n",
    "To make the value of $q_{0}$ random, the first thing we'll do is apply a Hadamard on that qubit. Next, we'll measure $q_{0}$ into $b_{0}$. After that, the dynamic magic should happen, and finally, $q_{1}$ gets measured."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd70af27",
   "metadata": {},
   "source": [
    "For this exercise we encourage you to read the [`if_test()`](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.if_test.html) documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97acd850",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 556.56x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qr = QuantumRegister(2)\n",
    "cr = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(qr, cr)\n",
    "\n",
    "q0, q1 = qr\n",
    "b0, b1 = cr\n",
    "\n",
    "qc.h(q0)\n",
    "qc.measure(q0, b0)\n",
    "\n",
    "####### your code goes here #######\n",
    "with qc.if_test((b0, 0)) as else_:\n",
    "    qc.x(q1)\n",
    "with else_:\n",
    "    qc.h(q1)\n",
    "\n",
    "\n",
    "qc.measure(q1, b1)\n",
    "qc.draw(output=\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff212ad",
   "metadata": {},
   "source": [
    "Optionally, you can run next cell to check if your circuit behaves as expected. \n",
    ">Tip: Think of the possible outcomes of the circuit before running it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e25f4018",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts: {'01': 244, '11': 264, '10': 516}\n"
     ]
    }
   ],
   "source": [
    "backend_sim = AerSimulator()\n",
    "\n",
    "job_1 = backend_sim.run(qc)\n",
    "result_1 = job_1.result()\n",
    "counts_1 = result_1.get_counts()\n",
    "\n",
    "print(f\"Counts: {counts_1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52ffa93c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex1b\n",
    "\n",
    "grade_ex1b(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accf02df-d743-4550-a165-eb7fa5563b92",
   "metadata": {},
   "source": [
    "## Repeat until success"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b636a19",
   "metadata": {},
   "source": [
    "Sometimes the outcome of a process is random and the result you get isn't what you wanted. What can you do in this case? Well, you can try again! As long as there is some nonzero probability of your desired outcome, repeating the process is guaranteed to return the result you wanted, eventually. Often, only a few repetitions will be needed.\n",
    "\n",
    "In this section, we will use the repeat-until-success idea to build an $R_X(\\theta)$ gate from the finite gate set $\\{H,\\,X,\\,S,\\,\\text{Toffoli}\\}$, where $\\theta / \\pi$ is an irrational number. Specifically, we will have $\\cos\\theta = \\frac35$. This construction is adapted from Nielsen and Chuang, _Quantum Computation and Quantum Information_, 10<sup>th</sup> anniversary edition, Exercise 4.41, and it demonstrates the universality of the finite gate set given above.\n",
    "\n",
    "The way the construction works is that we will build a circuit that acts on 3 qubits. One of the qubits is the target qubit and our goal is to perform the $R_X(\\theta)$ on this target qubit. The other two qubits are \"syndrome\" qubits that we will measure and the measurement results will tell us whether our operation was successful or not. If both syndrome qubits read 0, then we will know the gate was performed successfully. Otherwise, we will reset the qubits and try again. We will need dynamic circuits in order to condition the repetitions of the circuit on the measurement results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319aa2d5",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e744fd1b",
   "metadata": {},
   "source": [
    "Your first task of this section will be to create a function that returns that circuit. Here there's an image of how the circuit should look like:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eaf0de0",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"resources/circuitRx.png\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0057b5c",
   "metadata": {},
   "source": [
    "To make things easier for you, we'll set up the \"base\" circuit for you with the required elements. The functions we'll write in this lab will take a circuit as input and modify it in place. Whenever we need a fresh circuit, we'll just make a copy of the base circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a5587d76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "controls = QuantumRegister(2, name=\"control\")\n",
    "target = QuantumRegister(1, name=\"target\")\n",
    "\n",
    "mid_measure = ClassicalRegister(2, name=\"mid\")\n",
    "final_measure = ClassicalRegister(1, name=\"final\")\n",
    "\n",
    "base = QuantumCircuit(controls, target, mid_measure, final_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976de85c",
   "metadata": {},
   "source": [
    "In the next cell, fill in the `trial` function so that it constructs the circuit by taking the following steps:\n",
    "\n",
    "1. Apply a Hadamard gate to each qubit of the control register as well as the target qubit.\n",
    "2. Apply the Toffoli (controlled-controlled-not) gate between the control register and the target qubit. This can be achieved using either the `ccx` method of QuantumCircuit, or by importing and using `CCXGate` from `qiskit.circuit.library`.\n",
    "3. Apply an $S$ gate to the target qubit.\n",
    "4. Apply another Toffoli gate, with the same controls and target as Step 2.\n",
    "5. Again, apply Hadamard to the control and target registers.\n",
    "6. Measure the control register into the classical register."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc47a87d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 787.878x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def trial(\n",
    "    circuit: QuantumCircuit,\n",
    "    target: QuantumRegister,\n",
    "    controls: QuantumRegister,\n",
    "    measures: ClassicalRegister,\n",
    "):\n",
    "    \n",
    "    \"\"\"Probabilistically perform Rx(theta) on the target, where cos(theta) = 3/5.\"\"\"\n",
    "\n",
    "    \n",
    "\n",
    "    circuit.h(controls[0])\n",
    "    circuit.h(controls[1])\n",
    "    circuit.h(target[0])\n",
    "    circuit.ccx(controls[0],controls[1],target[0],ctrl_state=None)\n",
    "    circuit.s(target[0])\n",
    "    circuit.ccx(controls[0],controls[1],target[0],ctrl_state=None)\n",
    "    circuit.h(controls[0])\n",
    "    circuit.h(controls[1])\n",
    "    circuit.h(target[0])\n",
    "    circuit.measure(controls,measures)\n",
    "    \n",
    "    return circuit\n",
    "\n",
    "\n",
    "qc = base.copy_empty_like()\n",
    "trial(qc, target, controls, mid_measure)\n",
    "qc.draw(\"mpl\", cregbundle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "029c1c44",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "from qc_grader.challenges.spring_2023 import grade_ex1c\n",
    "\n",
    "grade_ex1c(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72855e8b",
   "metadata": {},
   "source": [
    "### Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e421924e",
   "metadata": {},
   "source": [
    "Your next task will be to check the measurements. If both measurements of the control bits return $\\ket{0}$, the applied gate is $R_X(\\theta)$ where $\\cos(\\theta) = \\frac{3}{5}$. If any of the measurements is $\\ket{1}$, then the applied gate is simply $X$, which indicates a failure. Therefore, we can tell from the measurements whether we applied the correct gate, without disturbing the coherence of the target qubit. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1a4ee1",
   "metadata": {},
   "source": [
    "If there is a failure, we should reset the qubit and start again.  Since we know what is applied in the case of a failure, we can use this knowledge to perform the reset efficiently, without using a general hardware reset. For the two auxiliary qubits, this is just an $X$ gate conditioned on its respective measurement being $1$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40077b6",
   "metadata": {},
   "source": [
    "In the next cell, fill in the `reset_controls` function so that it modifies the circuit by taking the following steps:\n",
    "\n",
    "1. If the first bit of the `measures` register is equal to 1, apply an $X$ gate to the first control qubit.\n",
    "2. If the second bit of the `measures` register is equal to 1, apply an $X$ gate to the second control qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "73ba4506",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rr6zRugsLC7V9+3abifUk6a233tLhw4e1atUq6wchLVq0UJ8+ffTtt9+WuTdxVWVkZFjrqCov35rtIgwOj1VU56E12uaFjqQfUVF+bq3uoyp8vPyqFeYlqajA4vDPCnVPeEBctbfNzc6vE+cArwEV8/b21uTJkyWVvKb/+c9/rvYfceWF+dIx8y+88IIeeugheXl56Y477rAZrnah9PT0atWAmufnXfHvkK+Pv85lH7W7rKCoZAIW30racEfpR44or9D17+VAVUydOtX6/xkzZlT7Nbi8MF86Zn7y5MlatWqVJOmuu+6qMNDzPuAabhfoS3sPWrduXaX1S/94vfC+vedbs2aNpD9moJeklJQUFRUVKSoqqtxL0jdtKplpzNFAv337dhUUFCg6OlpNmjSp8boLCgrUrl07m9n1Fy9erH79+tlc1dC7d2+1bt1aixYtqnagL70MyBGeBrw5evOI5nWmh76ouFBent4Ob+fpI0VGRtZCRXCmgIZ+la9UjgaBPnXiHOA1oKRnprw/jMaOHatmzZpJkr7++usyw6qqqrIwL0mzZ8/WAw88YA30Tz31lAoLC+22FxERQc9MHeHjVfHvUGhQcx3MTFFBUX6Zy+6PnzmsRgFh9a53XpIimjenhx51RkXvA02aNNH48SVXnh0/flwfffRRtfZRWZiXpJ9//lm///67evTooe7du6tnz55av97+PBu8D1yc6uQmyQ0DfU5OjiTp3LmqvSCX9mqUbneh9PR0LVq0SF5eXho7dqz1+dLL7RMTE8utY/fu3fL29lbnzp2tz5fe6i44OFgxMTF2ty0N/OW1XRN1X/hhQkpKivWF4XydOnWyXi1QHdW5bCS/SJrxWbV36RK7du+Wbx35bdr4pXTygOPbPfrcXXol+q7KV0SdVlwo/fymVFTFuxyU8vCSvv3xP/Ku/ucBNYbXgJLX9vMnpzvf+a/pc+bMqVb7VQnzUsl7yddff63x48crPDxcffr0sd4O9kK7d+8udwgYnKu4QFrxWvnL27fsqQ27lmrnwd8U37q/9fmCwjztO5Kk+NYDnFBl3bN712551r/PMVBHVfQ+cOWVV1qH1r733nvKz893uP2qhPlSc+bMsc7bcu2115Yb6HkfcA23+wiltMe69JL0ypTeh33jxrJTPJvNZt15553Kz8/XbbfdpqZNm1qXVRboN2/eLLPZrI4dO9pMzFfeuPrzVdb2xdRd3oR4p06dsjvjfuPGjXXy5Mly60Dd06Kr49v4h0iNK59yAgbg6S0171z5ehcK76g6EeZRuR49ekiScnNz9fPPPzu8fVXDfKn//ve/1v93797d8YJR5wzqUnJXngU/v2Lz/Pfr3lZeYa6GJN7kmsIAVEnp+4AkLVmyxOHtHQnzF+6D94G6x+0C/TXXXCNJmjlzppYuXWqz7PDhw3r66ae1e/cfE8Vcd911kqRnn33Wpic6PT1d1113nb799lvFxcXp2WeftWmrstBdern9hctLP7VKS0srt3e9Kj30F1t3fbllXX0U1loKcPDGBNGXSP9/x0W4gRZdS4J9VZk8pahutVYOalBISIh1SFlSUpLDsw07GuYlacOGDdb/84ece2gVEa+r+0zV6q0L9MSHY/X9unc0d9F9mrvo70poPVBDEm90dYkAKnD+a7G9zr2KOBrmpZJ8UXr5f+ldtFB31JGLhGvO/fffr0WLFmnjxo0aMWKEwsPD1bJlS508eVL79u2TyWTStGnTrOtPnTpVX375pX755RclJCSobdu28vHx0fbt21VUVKQePXro66+/LtN7XdVAf2Fw7tSpk6KionTw4EFFRUWpXbt28vT01M0332y9b3xVeugvtu4Le+hDQkJ0+vTpMvs5efKkGjduXG4dqHs8PKSu10i/z5fyq3Ab0pbdq9eji7rLP1iKv0pK/qZk9voKmaROV0iB9qfrQB1z/vwwW7ZscWjb6oR5Sdq2bZvMZrM8PDysV4fB+KZc/YqahcTo+3Vv6bft3ykoIExj+t6tW0b8kzGwQB1X+lqcmpqqs2fPVnm76oT5Ulu2bFFERIQaN26skJAQnTp1qnrFo8a5XaAPDAzUzz//rJdffllffPGFdu3apTNnzig8PFxjxozR2LFjbcZ2+Pj46IcfftCsWbM0f/587d+/Xw0bNlTv3r1144036vbbb5e3t21XV1pamk6ePKnQ0NByJ6UrL9D7+flpyZIlevjhh7V27VqtW7dOFovFGubT0tJ04sQJhYWFqUWLFuUeZ3XrLm37womvOnbsaHesfEpKigYMMOZYuhZxgzTt44pnfq5suVE1CJZ63iSl/Fc6mWp/HS8/qVUvKaoHvfPuKKy11G28tGOplFPOqJkGwVL7y6SwVk4tzWnc8TUgNzdX//vf/9SgQQOH5jfx9fXV8uXLHQ7zklRUVKSVK1fKYrFY7+4C4/P08NT4gfdp/MD7XF0KAAetWrVKISEhDs8q/+qrr1YrzEslVwL4+vpWeZ4yOI/JUt173cCtvPDCC3r44Ye1b98+6wcJ69at06WXXqoFCxZYhzI4gxEnxHp2gurMpHgXyjkhpSWXfFmKJQ9PqcNQqVkHxy7LhjFZLNKpQ9KRrVLmDsliLpkAL2G0FBpTNz/M4TWg4smQqmvq1Kl6/fXXHQrzjsjOzmYypDqisknxYN/ge8SkeKgzauN9oHv37vrhhx8UGBjoUJivKt4HXKOORhA42+TJkzV79myNHj1aTz75pPLy8jR9+nRdcsklGj16tKvLw0UICJXaD5GO7iq5BN+7gdQ83tVVwVlMppIJDxtHSacO/v854Oe+vfIo3xtvvKGcnBz99NNPNR7mAQB134YNGzRs2DA1b95cixYtcnU5qCEEekiSgoKCtHz5ck2bNk3XX3+9vLy8NGrUKL388suMpQMAN/HBBx+4ugQAgAtt2LDBZrJTGB+BHlZt2rTR4sWLXV0GAAAAAKAK6HoFAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAARHoAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAPycnUBwIV8PKVnJ7i6Csf4eLq6AsB98Bog+fv7Kzs7u8bae/7N+Tqbk6ugAH898JfryzyuCf7+/jXSDgCA9wFUHYEedY7JJPlyZgL1Fq8BkslkUkBAQI215+PrJ5/CYvn4+ikgIKDMYwBA3cL7AKqKS+4BAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAbk5eoCgAtZLFJBsaurcIyPp2QyuboKwD3wGgCLxaLc3FxXl+EQf39/mTgJUEdYLJK50NVVGI+HN6/ldYnR3gtc9T5AoEedU1AszfjM1VU45tkJki+/TUCN4DUAubm5CgwMdHUZDsnOzlZAQICrywAklYT5Fa+5ugrjGXyP5Onj6ipQymjvBa56H+CSewAAAAAADIhADwAAAACAARHoAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAARHo6zGLxaKgoCCZTCYdP37c1eUAAFBnde3aVQEBAa4uAwDgIo0bN1bHjh1dXUYZXq4uoL574YUXlJ2drXvvvVeNGjVy6r737t2rrKwsRUZGKiwszKn7BgCgtvXq1Ut9+vRR9+7d1aVLFwUHB8vT01O5ubnatWuXNmzYoN9//11Lly7VuXPnym3n0ksv1f/+9z8lJyfriiuuUE5OjhOPAgBQXTExMbrsssvUvXt3devWTREREfLx8VF+fr4OHz6s33//XRs2bNCPP/6oI0eOlNtO48aNtWzZMkVGRmrIkCHaunWrE4+iYgR6Fzpz5oymT58uPz8/Pfroo07ff3JysqSSXgcAANxBw4YNNXHiRP31r39Vp06dyl2vTZs2uuKKKyRJp06d0ocffqg5c+Zo165dNuuVhvmgoCD1799fjz76qB588MFaPQYAQPV5eHho5MiR+utf/6oRI0bIw8P+RenR0dHq06ePJKmoqEjffvut/v3vf2vZsmU265WG+dLM9PHHHysxMVEWi6VWj6OquOTehTZu3CiLxaL4+Hh5eno6ff9JSUmSpC5dujh9386QlvKTXr3ZpA3fvVDuOq/ebNI3L4xyYlXOZ7FIpw5JxQUlj4sLpXw6l+oVc5F0dLdUVHoOFJWcB+6O14D6Z/To0dq1a5feeOONMmG+tDfm4MGDOnPmjM2ykJAQ/e1vf1NKSoqee+45+fn5SbIN85L0ww8/6Mknn3TOwQC1zGw266tVL+u25zpo5EN+uvFfLTV30X06V8AfCTCu9u3ba/Xq1Vq0aJGuuOKKMmE+IyNDqampyszMtHney8tLY8eO1Y8//qilS5cqKipKUtkwf+TIEV133XV1JsxLBHqX2rhxoyQpMTHRJfunh969WczSoU3Sr+9LGz77I8wV5Uur35S2LJayjrm2RtSuwjxpz8/Sz29Km7/540OdoryS53atkPi7De4gMDBQH3/8sRYuXKjw8HDr8z///LMmT56srl27KjAwUC1atFB0dLSCg4MVHR2tsWPH6v3337debu/p6akHHnhAmzZt0i233FImzI8ePbrCS/MBI5mz6F7NXfR3RTWL011jZmtAwngtXP2aHnvvKpnNZleXBzjs3nvvVVJSknr37m19bv/+/frHP/6hgQMHKigoSBEREYqJiVF4eLhCQkJ02WWX6amnnrK53H7YsGHasmWLpk6dWibMDx48uMyVXK7m9oHebDbryy+/1JgxY9S8eXP5+vqqefPmGjJkiF599VUVFBTYrJ+dna3nnntOPXv2VEhIiBo0aKBu3bpp7ty55X4SEx4eLpPJpAMHDigpKUkTJ05UZGSkAgIC1LVrV82fP99m/a+++komk0n333+/JOnNN9+UyWSyfj399NPWdWNiYmQymbRjxw6tXLlSV199tZo0aSKTyaT333//oup29x76+sxcLG1eJO1cJuWeLLvcYpYyd0jr50nH9zm/PtS+vCzp90+lA+ukQjv5oyhPOrhBWv+JlHva6eUBNSYkJETLli3TTTfdZH1u8eLF6tKliwYMGKC3335bycnJKioqstnu4MGD+vrrr3XbbbcpMjJSjz32mPLz8yVJHTp00Pvvv0+Yh9s6kLFN36yZrX6dx+qJWxZoZK87dOfVL+nOq15S0t4V+il5fuWNAHXIq6++qpdeesl6hdWuXbt01VVXqW3btpo5c6ZWrVqlrKwsm21Onz6t5cuX67HHHlN0dLSuv/56paWlSZKCgoL0+uuv1/kwL7l5oD969KiGDBmi8ePH65tvvpG3t7e6du0qb29vrVixQg899JDNpe7Jycnq0qWLZsyYoW3btik6Olrh4eFKSkrSlClTdOutt5bZR2ZmpjIzMxUUFKSFCxeqR48eWrp0qZo3by4/Pz8lJyfrhhtu0NKlS63b5OTkqG/fvtYTLjExUX379rV+lY7lOH36tFJTU9WgQQN99tlnGjx4sH799VfFxMSoadOmSkhIqHbdp06d0qFDhxQQEKC2bdvW6Pcdrrd9qXRsd+XrmYukzd9KZzNqvyY4T1GBlLRAyjlR+brnzkibvrQf+oG6LjAwUEuWLNEll1wiqeR9c9KkSbrqqqu0efPmKrdz6tQpPfXUU0pMTFRKSookyWQySZJ+++03wjzczoqkT2WxWDS2/99snh/Z6w75efvrx40fu6YwoBpeeOEF3XPPPdbHL730krp27arFixdX+WqToqIiffbZZ+rUqZM+/tj2/D979mydDfOSGwf6nJwcjRw5UitXrtTAgQOVnJys1NRUrVu3Tqmpqdq6dasefPBBa6Dfv3+/RowYoX379mnatGlKT09XUlKS9u/fr+XLl8vf318ffvihFi5caLOf0svW8/Ly9OSTT2revHnKyMjQ+vXrdfjwYY0aVTI2891337VuM2nSJK1atco6pmP58uVavXq19WvQoEGS/uhBz8/P1zPPPKO5c+da205PT1diYmK16y5tOz4+vtyJItxFUUGuzmUdt/vljrKOSunbqr6+uUjau7r26oHzpW+Tsh0YTnHutJSWXGvluFx9ew2oT2bPnq1evXpJKhkX2a9fP/3nP/+pdnuNGjVSixYtbJ5r2bKl9QN4wF3sPLReHiYPtY+6xOZ5H28/tW7eVbsOrXdRZYBjJkyYoPvuu09SyZXZt9xyi+67775qfwjr5eWlzp072zwXFBSkyMjIi661trjtLPf33XefNmzYoH79+mnp0qXy8fGxWd6pUyebCXNuvfVWZWZm6t5779VLL71ks+6gQYN055136qWXXtL8+fM1ZswY67LSQF9YWKhPPvnEOmOuJPn5+WnKlClavHixUlNTbdrctWuXcnNzrWP57Clt22w2a86cOTY97aUh/GLrrg/j59d+9bjWfvW4q8twmuoEsxMHpNxTkn9IjZcDJ7NYpLQkx7dLS5aiL5Hc8fO9+vYaUF+MHDlSf/rTnySV3DVm6NCh2rbNgU8zL3DhBHgnT55U48aNFRERoddee00TJ06sibKBOuHE2SMKCgiTj5dvmWVhjSKVkvqLCosK5O3lY2droG5o2rSpXn/9devjO++8Ux999FG127twAryzZ89a3xPee+89xcfHKzs7+6Jqrg1uGeh37Nihd955R76+vpo3b16ZMH+hVatWaeXKlQoODtZTTz1ld51u3bpJKunJP19pMB43bpxNmC9VOkb/wtBe2kNeUaAuXWfIkCF2L5u/mLrtjZ9PS0vTrFmz9Ntvvyk5OVkFBQV1agbH6uo8eLJie423u+zrWcOcXE3ty9xRze12Sq0urdla4Hw5x6t2qf2F8rOkM4elkJY1X5Or1bfXgPqgQYMGeuutt6yP77333hoN8z/88IPuvPNO/f777woJCdHNN9+sefPm6b///e9F1w7UBfkFufK2E+Ylycer5IqU/MJcAj3qtFdeeUVhYWGSpM8//1xvv/12tduyN5v9kCFDNGfOHA0ePFgxMTH617/+pb/97W81UHnNcstA/8knn6i4uFiTJk2y3nKgIp999pmkkt7ugIAAu+uUfihw4WV3pYF+woQJdrfbuXOnJKldu3Y2zzsS6MvrFbiYuu3tf8+ePfrqq6/Us2dP+fj4aM2aNeXWVlU9evRQRoZjA7Q9vRvomn9VYQB4FQWHxyqq89Aaa8+edrGxKq4Dg5C9PH30zt3Vm+Vu7uz3NG/cYzVcEZwtrmU/Tb+2epMZ3TbpL1q/+7sarshxvAbUvGtu/ZsCAoOUnpGuFi1alHlc11Q25vGGG26wXv64ZMkSm0liHWUvzJeOmZ82bZq1t+e+++6rMNDHxsbWmSFsPl4N9NZdNfc7VF/EtotVQVHd/T12RGXngK+Pv85lH7W7rKAor2Qdb/9aqa0uc6dz4EJGex+QKn4viIqK0nXXXSdJOnbsmKZOnVrt/dgL86Vj5m+//XZt2bJFAQEB+vOf/6zHH3+8zK1PS13s+0B4eLh+//13h7dzy0D/448/Siq5HK8qfvnlF0nSZZddVu46hw8fliRFR0dbn8vPz9eOHSXdoaXj3i9UXnCvLNAXFhZaJ+a58sora7TuwsJCbd++XR4eHtaJ9SRpwIABSk9PlyQ98cQTNRLoMzIyrDVUlZev8d5AjqQfUVF+rqvLkIeHZ+UrlePM2VMO/6xQ94T6pld728xjjv++1gZeA2qeubjY+u/hw4fLPDaa8/9we/TRR6vdTkVhXpI+/vhj/eMf/1C7du00dOhQtW/f3vpB/YVK3z/rAr96GMRqQvqRI8orrLu/x46o7BwIDWqug5kpKijKL3PZ/fEzh9UoIKxe9s670zlwIXd7H5g8ebJ1LrRXX31Vx49Xb16cisK8VHKV83vvvae7775bAQEBmjRpkmbPnm23LVe9D7hloD906JAkqXXr1lVav/Qkbtmy/GtNS8Nt6Qz0kpSSkqKioiJFRUUpNDTU7nabNm2S5Hig3759uwoKChQdHa0mTZrUeN0FBQVq166d/P3/eMGvjZ6F8+8HXFWe3g1qvI7a1jyieZ3pnTt6+oCaBsc4vF1O0bE6PeEHqsbsnSuzxSwPU9V/ny0Wi0wmk4o8ztaJc4DXgJrn8f9/9Hh4eioyMrLM47rGbDaX+4dRly5drMPJ1q9frw0bNlRrH5WFeankd2Pu3LnWOWpuvfVWPfjgg3bbi4iIqFM99HBcRPPmbtM7W9k50L5lT23YtVQ7D/6m+Nb9rc8XFOZp35EkxbceUNsl1knudA5cyGjvA1LF7wW33XabpJKOynfeeada7VcW5kvNmTNHd999tyTp9ttvLzfQX+z7QHVyk+SmgT4nJ0eSqjy7Yek48dLtLpSenq5FixbJy8tLY8eOtT5ferl9YmJiuXXs3r1b3t7eNrMllt7qLjg4WDExMXa3LQ385bVdE3U7Y0K86lw2kl8kzfisFoqpRbt275ZvHfltOrBO2vOzY9t4ekvvL5wlL99ZtVMUnGrTV9KJ/ZWvV8pkMqlRc+m3zStqrygH8BpQ855+Y57OZucoIjxCaWlpZR7XNTk5OQoMDLS7rHfv3tb/z5s3r1rtVyXMn7+P0kB/6aXlTzSye/fucoe/OVtxgbTiNVdXYTy7d+2Wp5t0Sld2DgzqMkGfLn9aC35+xSbQf7/ubeUV5mpI4k1OqLLucadz4EJGex+Qyn8viI6OVkREhCRp2bJlyszMdLjtqoZ5qaSjdePGjerWrZvi4+MVGBhod3I8V70P1I2PkmtYaY916SXplSm9D/vGjRvLLDObzbrzzjuVn5+v2267TU2bNrUuqyzQb968WWazWR07drSZmK+8cfXnq6zti6nb3oR4cB/N4yWTg1feh8dJ5cyNAwNq2dXxbVpUYxvAFbp37279/2+//ebw9o6EeUk6evSo9U41iYmJ1vvTA0bWKiJeV/eZqtVbF+iJD8fq+3XvaO6i+zR30d+V0HqghiTe6OoSgXKd/z6wfr3jt1h0JMxfuB8PD486d5cwtwz011xzjSRp5syZWrp0qc2yw4cP6+mnn9bu3X9MFFI6ocKzzz5rHbculfRwX3fddfr2228VFxenZ5991qatykJ36eX2Fy4v/eQmLS2t3N71qvTQX2zdde1kRM3w8Zc6OjBxt39jqW2/2qsHzhfauuSDnapq2k4K71h79QA1qXTuF7PZbH0/qypHw3yp0sv6g4KC1KpVq2pUDdQ9U65+RZNHvaDUzG16/eup+ilpvsb0vVv/um1xnRk+Athz/hxg9jo2K1KdMC/JZnhXXesUrcMXCFbf/fffr0WLFmnjxo0aMWKEwsPD1bJlS508eVL79u2TyWTStGnTrOtPnTpVX375pX755RclJCSobdu28vHx0fbt21VUVKQePXro66+/LnPruaoG+guDc6dOnRQVFaWDBw8qKipK7dq1k6enp26++WbdeeedVWq7JuquaydjTWsRN0jTPq74tnuVLTeq5p1L7ke+Y2nJv+UJbColjpUMOGQZFTCZpA7DJJOHdLiSvBPeUYobUbKNu6nPrwHurDSMnz17Vrm5VZ+8qrphXpLN5Zyl2wNG5+nhqfED79P4gfe5uhTAIY0aNbL+35G7aVU3zEu27wMNGzaserFO4JYfvwUGBurnn3/Wv/71L3Xp0kVnzpzR1q1bZTabNWbMGH344Yc24xt8fHz0ww8/6NFHH1Xr1q21f/9+HTlyRL1799acOXP0yy+/lLmdQ1pamk6ePKnQ0NByJ6UrL9D7+flpyZIlGj16tLy9vbVu3TqtWbPGOkYkLS1NJ06cUFhYWIW3kahu3aVt19UJMFAzIuOl3rdL0T3LBvbgFlLnK6VLbpR87Q9ThcF5eEgdhko9bywZUnHhMIxm7aXuE6ROIyUPt/xoF+5qwIABiomJUc+ePR3aLiEhoVphXpKefPJJtWnTRpGRkdq6davDNQMAas5jjz1m7RQtvaq5Klq2bGm985cjYV6Sli9frtjYWLVs2bLcSfFcxW3/jPP399cjjzyiRx55pMrr//Of/9Q///nPKq3fokUL66R05aloQri4uDgtXLiw2m2Xqo264T78g6XYgVKbftLqN6WCXMknQOpxvasrgzOYTFKj5iVfHYZKv7zzxzkQf5WrqwOq59ixY9Xa7q233pKXl5fGjBnjUJiXVK0JlwAAtSMrK0tZWVkOb5ecnKxhw4bpww8/1NixY6sc5iUpOztbe/bscXifzuC2gR7V8+WXX0qSdUx+6eOYmBj16NHDZXXh4nh4llx+LbnnpdWonJcP5wDw73//W3PnzpXZbHZ1KQAAF9iwYYMSEhLc6n2AQA8b48ePt/v4lltu0QcffOCCigAAqDnu9EccAMBx7vY+QKCHDS7HBwAAAABjcMtJ8QAAAAAAcHcEegAAAAAADIhADwAAAACAARHoAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAAXm5ugDgQj6e0rMTXF2FY3w8XV0B4D54DYC/v7+ys7NrrL3n35yvszm5Cgrw1wN/ub7M45rg7+9fI+0AAErU5HuBvdf9mn4vcNX7AIEedY7JJPlyZgL1Fq8BMJlMCggIqLH2fHz95FNYLB9fPwUEBJR5DMflFeTq3e8f1JptC3XizGE1CW6pjx8+UOP7+WjpE/rPD0/qPw/tV3jjmBpvH0DdVZPvBfZe993lvYA/mQAAAFCu5L0/6f65gzX5yuc1ftD9kqTPVjyrhWtma/zA+9UqIkH+vg1dXCUA1E8EegAAADhk4+4f1Co8XpNHPe/qUgCgXmNSPAAAADjkZFaGGvo3dnUZAFDv0UMPAACAKvnf+g/0wue3SpIyTu7XsAdMkqSJwx7XpOFPVLmdtGO79fGP/9Sm3T/qbO4JhQY114CE8Zo4/Ak18Kl4LOvZ3JOa9+NT+nXbtzp+9rD8fAIUHhKjQV2v13WDHrBZ96ekz7RwzWztS0+W2VysVuHxGj/oAQ1IGOfYgQNAHUWgBwAAQJUktB6gGdf/R3MX3auggDDdOOQRSVLriIQqt7ErbYMeeHOIAv2CdeWlf1FYo0jtPZKshatf07YDa/TilJXy8vQud/un/jNeW/at0qjed6p1RILyC8/pYOZ2Je/9ySbQv//ff+iTZTPVs/3l+tOIp2QyeWjN1q/11H/G664xr2t036nV/0YAQB1BoAcAAECVRIS2VkRoa33wv38oJLCZhna/2eE2Xvz8NoU2jNDr96yXv98fk+kltr1MT340Vss2ztOInn+yu23OuTNK2rNcV/WeorvGzC53H7vTNuqTZTN1/ZCHdPsVT1ufv6bfPXr8gzF6b8lDGtZ9ks3+AcCIGEMPAAAAp9ifvkX70jdrcOKNKizO15mc49avzq36yc8nQBt2LS13ex/vBvL28tWOg+uUcfJAuest2zRPJpNJw7vfYrOPMznH1TvuauXmZykl9ddaOEIAcC566AEAAOAUB49ulyR9tPRxfbT0cbvrnMrOLHd7by8fTbn6Fc35ZpomPtNK0c3i1LXtEPXpNEbdYi+z2Y/FYtFtz3cot62K9gMARkGgBwAAgFNYLBZJ0rgB96ln+8vtrhPoH1JhG1f1vlN9Oo3Wuu3fafO+lfp585f6Zs3rGtRlgh65eX7pjmQymTTz9iXyNHnabSc6vFP1DwQA6ggCPQAAAJwiMixWkuTh4alu7YZWu53QoAiN7PVnjez1ZxWbi/XspxO1IulTjRt4n9q37KnIsFit3/lfNQ2OUnSzjjVVPgDUOYyhBwAAgFO0jUxUTHhnLV47V+kn9pVZXlxcpLO5J8vdPq8gV3kFuTbPeXp4WmfZL912aPeJkqT3lzysYnNxmXZOZXG5PQD3QA89AAAAnMJkMmnG9f/R9DeHaPJLCbq8522KbtZJ+YW5Onx8j9ZsXaDbrnim3Fnu047t0n1zB6pvp2vUKryzAv1DdDBzuxb/OkfhjVspvlV/SVL7lj01adgT+uiHJ3Tny101IGG8QoOa6+TZdO0+vEG/7fheS2YVOPHIAaB2EOgBAADgNG0ju2rOvZv06fJn9GvKt1q8dq4a+DZUeEiMhvf4kxLPm9zuQk2DW+rynrcpac8K/bJtoQqL8hXWKFJX9LpDEwbPkJ+Pv3XdicMfV7uWPfT16tf09c+vKK8gR8GBTRUT3ll/Hf2aMw4VAGodgR4AAADl6tJmkH543mLz3McPH7ioNpuFROtv186tdL1Jw5/QpOFPWB8HBYRqytUvV3k/vTpeqV4dr6xOiQBgCIyhBwAAAADAgOihBwAAwEXJOXdG+YXnKlzHy8tHQf6NnVQRANQPBHoAAABclDe+maYfNnxY4ToJrQfqxSk/OacgAKgnCPSocywWqaDsHWbqNB9PyWRydRXug3OgfuPnDxjPhMHTNbTbzRWuE+gf4qRqAKD+INCjzikolmZ85uoqHPPsBMmX36YawzlQv/HzB4wnulmcopvFuboMAKh3mBQPAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCDm5AXcVH62dPqIlJVZ8lWQW/J8Qa609XspqNn/f0VIHp6urRW1I+eEdCZdOpsp5Ry3PQd2/CA1DJcahUsBYdxyDQAAwIgI9IAbsVikk6lSWpJ0bK8ki511zFJGSsmXJPkGSpEJJV++gc6sFrWhuFDK3FlyDpzNsL+OxSylJUtKLnkc2ERq0VUK7yh5+TipUAAAAFw0Aj3gJrKPSyn/LT/ElSc/W9r3i7R/rRTTS2p1KT32RnVsr7R9qVSQ49h22cdKeuz3rpbaXyY1a0+PPQAAgBEQ6AGDs1ikA7+VhHJL8UW0Y5b2/yod2yN1Gik1bFJzNaJ2FeVLO5b9cdVFdRWek7YuLunh7zhM8vGvmfoAAABQO5gUDzAws1natkTa+/PFhfnzZR+Tfv9EOnmwZtpD7SrIkX7/7OLD/PmO7ZZ+ny/lna25NgEAAFDzCPRV8MILL+iJJ57QmTNnXF2KU9S34zUqi6XkEvuaDHKligulpAXS6cM13zZqTmGetPFLKftozbede1La8LmU7+Dl+wAAAHAeAn0lzpw5o+nTp+u5555TYKD7zxhW347XyFLX106YL2UukpIXOj4eG85hsUjbvi+5oqK2nDstbVlUsi8AAADUPYyhr8TGjRtlsVgUHx8vT0/3nymsvh2vUeWckPatcWybS26WfAJKAvpvH1dtm8JzJWOzE652vEbUrvQU6fg+x7apzjlwOk06tEmK6uZ4jQAAAKhd9NBXYuPGjZKkxMREF1fiHO50vGkpP+nVm03a8N0L5a7z6s0mffPCKCdWdfFKL7U3Ozhm3idA8mtY8q8jju6SMnc5tk1dcuboPi17d7I+eqCD3rjNX3Mnh+ij6R21dO4tOpSywtXlVUt+jrRruePbVfcc2LNKOmfAETju+hoAAABQikBfjq+++komk0n333+/JOnNN9+UyWSyfj399NOSpAMHDuj555/X5ZdfrjZt2iggIED+/v7q0qWLZs2apYKCArvtx8TEyGQyaceOHVq5cqWuvvpqNWnSRCaTSe+//77NuhkZGZo2bZpatWolPz8/xcbG6rnnnpPZbNaf/vQnmUwmvfPOO2X2UVBQoLfffltDhw5VaGiofH191bZtWz344IPKzs6u1vHC9U4dks6kO3efqb85d381JXPf7/r4oXjtWve5ouKHa8BNL6vX2MfVMm6Ijuz+RXvWf+XqEqvl8OaSme2dxVwkHdrovP0BAACgarjkvhw5OTnq27evNmzYoLy8PCUmJsrf/497OPXp00eS9NRTT+m9995TQECAmjdvrk6dOuno0aPavHmzNm/erPXr1+urr2xDw+nTp5WamqoGDRros88+05NPPqnQ0FDFxMTIw8NDCQkJ1nXXrVunkSNH6uTJkwoMDFSnTp2UlpamGTNmaP/+/dq+fbskqWvXrjb72LNnj8aMGaNt27bJy8tLrVu3VtOmTbVnzx49++yz+uGHH/Tzzz9bj6mqxwvXS0ty/j7PZpR8iNAowvn7vhjrvn5SRfm5unFmkppEdymzPOd0hguqujhms3Q42fn7PbJNatNP8vR2/r4BAABgH4G+HJMmTdLNN9+shg0bSpKWL1+u4ODgMusNHz5cU6dOVWJiokwmk/X533//XaNGjdKCBQu0ZcsWxcfHW5clJSVJkvLz8/XMM89o7ty5uv322+Xp6Smz2WxdLyMjQ6NHj9bJkyf14IMP6rHHHlODBg1ksVg0c+ZMPfbYY/L29panp6c6d+5s3e7kyZO64oortGfPHv35z3/WzJkz1bRpU0nS7t27NXr0aG3cuFEvv/yyHnnkEYeOF65VkFtyn3hXOLLFeIH+dMZu+QWG2g3zkhQQHO7kii7eyf1Sfnbl69W0ojzp6G4pIs75+wYAAIB9XHJfgV27dik3N1fR0dHlhtsJEyaoW7duNmFeknr06KFhw4ZJkrZt22azLDm5pHvNbDZrzpw5mjx5snUCOg8PD3l4lPxY7rrrLmVmZmrq1Kl65pln1KBBA0mSyWTSI488ori4OBUUFKhdu3by8/Oztj9t2jTt2bNH06ZN09tvv20N85IUGxur2bNnS5IWLlzo8PEaUVFBrs5lHbf7ZTRnMySLufL1aoMRb2HXqGkb5WWf0J71C1xdSo1x5c/BiOeA5F6vAQAAAOejh74CpT3pF17Ofr78/Hx9++23Wrlypfbv36+srCxrL3tpkPfx8bHb7pAhQ3TrrbfabXfr1q1asGCBgoOD9cwzz5RZbjKZ1KNHD23bts2mvm3btumTTz5ReHi43e3OP57Dh23/Oq/K8RrR2q8e19qvHnd1GTXibKbr9p1zUioukDx9Kl+3rrhkzD90cOsP+u7VaxUcHqvm7fqpWeueatFxkBpHdnR1edXiynMgy4X7vhju9BoAAABwPgJ9BSoLuMuWLdOtt96qQ4cOVdhO69at7bY7ceLEcreZP3++LBaLbrrpJutl8Bfy9fWVJHXp8sflxJ9++qnMZrOKi4utVwhcqHSivoAA26mu3TXQdx48WbG9xttd9vUs+9+juirrqAt3bpGyj0uNmruwBgdFxPbWDf/aoI3fv6gDyUuUsup9pawqmXSyefv+Gv6XD9SoaetKWqlbXHkOZB0ruULEZLBru9zpNQAAAOB8BPoKVBRwSyerKyws1O23366bbrpJnTt3VkhIiLy8vLRjxw517NhRXl5e6tjxj57AwsJCpaSkSJKuvPLKcve9fHnJPakuu+yyctc5cuSIJNtAX7rdsWPHdOzYsQqPLyYmxuZxbQT6Hj16KCPDsYnHPL0b6Jp/7a6xGoLDYxXVeWiNtWdPu9hYFReeq9V9SNL0az9TXMu+dpeV3mO8PL4Bf/zb7y8V76e8+5Rff91EbTlQ+7d6q8lzIKxlvIb/5QNJ0tnjqTq8faW2/vSOjuz8WYteGq0b/rVBnl4Xf9mBs86Bd+7eL69yZqarqXOgvJ+/pVhq26ad8gtzHajYcbwG1Lxrbv2bAgKDlJ6RrhYtWpR57O7q2/H7eDXQW3fV3O9QfRHbLlYFRXX399gRnAPV407nwIXq2+vghewdb137HoSHh+v33393eDsCfQUqCrgPPfSQCgoKNGvWLM2YMaPM8u+++06SFBcXZ+1Jl6Tt27eroKBA0dHRatKkSbn7Lu31j4qKsru8uLhY69atk2Qb6NPS0iSV3E4vOjq6gqMrqzYCfUZGRplL+yvj5etf+Up1zJH0IyrKr92QI0lFheXffL70HuOVMXlUbT17Tp887fDPszpq6xwICotWUP9J6tBvor54qr/Sd61Rxt7fFNm+30W37axzoLwwLznnHMjMOKqcvNq9KT2vATXPXFxs/ffw4cNlHru7+nb8ft7G+x2qC9KPHFFeLX9g6SycA9XjTufAherb6+CF7B2vu3wPCPTlyMzMVGZmpoKDg8v0ZBcXF2vVqlWSZHcMvNls1rx58yRJiYmJNstKQ/OFz18oJydHklRUVGR3+TfffKNjx46padOmioiIKLNdXl5ehe1fqKLjvRjh4Y7PIu7p3aDG9u8szSOaO6V3zuRZ/ox4BTkVb+sbUBLkLGYpv5J1y2urYaMARUZGVlLlxavtc8BkMim8TS+l71qjnFM18wLurHMgv/CcfMv5/tTUOVBRO2FNGyu4OLAKlVYfrwE1z6N04lVPT0VGRpZ57O7q2/H7eBnvd6guiGje3G16ZzkHqsedzoEL1bfXwQvZO9669j2oTm6SCPTl2rlzpySpXbt2ZZbl5OSo+P8/0bEXnF988UVt2rRJUtngXjrDfWWBPiIiQqdOndKGDRvUq1cvm2WnTp3SAw88IMm2d14q6dE/efKkfvzxR7Vv377CfZyvouO9GNW5bCS/SJrxWY2WUet27d4tXyf8Nu34sfz70Nu7RPp8/f5S0iubnyOtfrN6+//mv/PVoFH1tnVETZ0DqVt+UMu4wfLwtP3hFBWc08EtSyVJjSNr5j5szjoH1n5QMpeBPbV9Dvg2lA6k7nN8QwfxGlDznn5jns5m5ygiPEJpaWllHru7+nb8xQXSitdcXYXx7N6121ATv1aEc6B63OkcuFB9ex28kL3jdZfvgcGmNnKe0gnj0tLSrL3epYKCgqyf4sycOdMa7gsKCvT888/r0UcflZdXyV921e2hHzq0ZLzn008/bXPbux07dmj48OHat6/kj+oLA/24ceMklQwJ+PLLL8u0u2fPHj355JP66aefqny8qDsaNnPdvr39JL8g1+2/On6ed6/evaellr37FyUvfV3bVr6ntQue1LyHu+pE2lZ17DdJYS3jXV2mQ1x5DgS5cN8AAAAoi0Bfjk6dOikqKkpHjhxRVFSUevfurX79+mnu3LmSpEceeUSS9NZbbykyMlI9e/ZU06ZN9cgjj2j27NmyWCwymUxlxqNXtYd++vTpCg0N1eHDh9WlSxfFxcVZv8xms0aOHClJ6tatm812f//739W/f39lZWVp/PjxCg0NVffu3ZWYmKiwsDDFxsbqiSeeKDO+vrLjRd0QVL0rcWpEw2aSyeS6/VdH/5teUtueY5Wxd63WLnhCy96drOSlrykwpLmG3vGuhk1+39UlOszV5wAAAADqjjp8gaBr+fn5acmSJXr44Ye1du1arVu3ThaLRXfeeackacqUKfLy8tLzzz+vAwcOyNPTU6NGjdKMGTOst41r06aNgoL+6NJMS0vTiRMnFBYWVulMipGRkVq9erWmT5+un376SQcPHlSHDh308ssva8qUKWrTpo0kqV8/28m8GjRooGXLlmnOnDn69NNPlZKSoi1btigsLEwxMTG6+eabNWrUKLVq1cqh4zWiFnGDNO1jS4XrVLa8rgkMk/xDpNxTzt93s6qP4KgzouOHKzp+uKvLqFFN2kq7lksWF5y6zWp2RE6tc8fXAAAAgPMR6CsQFxenhQsXlrv8jjvu0B133GF3mcXOX9stWrSw+3x5OnTooG+//bbM86tXr1ZaWpq6deumli1bllnu7e2te+65R/fcc0+V9yVVfrxwPZNJatFV2lX7d46z4ekjhXdw7j5hn19DKaytdMzJdyMKiZICQp27TwAAAFSMS+4NJjc319prfvfdd7u4GrhCRCfJw8kfxTXvLLedJMaIWlY8YqdWtOjq/H0CAACgYgT6Ouof//iHjhw5YvNcSkqKRo4cqW3btql///6aNGmSi6qDK3n7SW37O29/Pv5S697O2x8qF9JSahrrvP01jnbu/gAAAFA1BPo6KDU1VTNnzlSLFi3Url07XXrppWrdurU6deqklStXqk+fPlqwYIE8PPjx1Vctu0nBFU/DUGM6DJcMeFtwt2YySR2GOufn4ukjdRxuvAkRAQAA6gMSYR3k7++ve++9V506ddLx48e1YcMGZWVlaejQoXrvvfe0cuVKhYWFubpMuJDJJMVd7ligK8iR8rJK/q2qyC5S07aO14fa5xNQcg7IgaBdnXOgw1CpQSOHywMAAIATMCleHdSkSRO99NJLri4DdZx/sJQ4Ttr4hVSUV/n6v33sWPvN2ksdLqtWaXCSJm2kuBFSyn+rtr6j50C7IVJEnON1AQAAwDnooQcMLKiZ1GNCycznNalFF6nzlZKJV4g6r3lnKX5UzU6UaPKUOo6QorrVXJsAAACoefTQAwYX2ES69E/S7pXS4c0X15ZvoNRxmBTWpkZKg5M06yA1DC/pqT+ddnFtBYWXXMofyKgeAACAOo9AD7gBL9+SicuadZAOrJNOpjq2vbef1DxeiulV8n8Yj3+w1H2CdGSLdHCDlHPCse0bBJf0yEd2lZhvEwAAwBgI9IAbaRxV8pVzUjqyWTp1WMo+JpmLyq7rGyg1bFZyO7Jm7SVPb+fXi5plMkmRCSUfzpxOk45sk86ml5wPspRd379xSY98RFzJremYyR4AAMBYCPSAGwpoLMUOKvm/uVjKPSkV5ksWc8lY6waNJN8Al5aIWmQyldyrPqRlyePigpJQX1woWSwlH94EhEpePq6tEwAAABeHQA+4OQ/PknH2qL88fUp64gEAAOBeGCkJAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwICYFA91jo+n9OwEV1fhGB9PV1fgXjgH6jd+/gAAAFVDoEedYzJJvpyZ9RrnQP3Gzx8AAKBquOQeAAAAgEPyCnL1xsJ7dOPMKI2Y7qmbn46plf18tPQJDXvApIyTB2qlfcDo6AMBAAAAUK7kvT/p/rmDNfnK5zV+0P2SpM9WPKuFa2Zr/MD71SoiQf6+DV1cJVA/EegBAAAAOGTj7h/UKjxek0c97+pSgHqNS+4BAAAAOORkVoYa+jd2dRlAvUcPPQAAAIAq+d/6D/TC57dKkjJO7tewB0ySpInDHtek4U9UuZ20Y7v18Y//1KbdP+ps7gmFBjXXgITxmjj8CTXwCahw27O5JzXvx6f067ZvdfzsYfn5BCg8JEaDul6v6wY9YLPuT0mfaeGa2dqXniyzuVitwuM1ftADGpAwzrEDB+ooAj0AAACAKkloPUAzrv+P5i66V0EBYbpxyCOSpNYRCVVuY1faBj3w5hAF+gXrykv/orBGkdp7JFkLV7+mbQfW6MUpK+Xl6V3u9k/9Z7y27FulUb3vVOuIBOUXntPBzO1K3vuTTaB//7//0CfLZqpn+8v1pxFPyWTy0JqtX+up/4zXXWNe1+i+U6v/jQDqCAI9AAAAgCqJCG2tiNDW+uB//1BIYDMN7X6zw228+PltCm0YodfvWS9/vz8m00tse5me/Gislm2cpxE9/2R325xzZ5S0Z7mu6j1Fd42ZXe4+dqdt1CfLZur6IQ/p9iuetj5/Tb979PgHY/Tekoc0rPskm/0DRsQYegAAAABOsT99i/alb9bgxBtVWJyvMznHrV+dW/WTn0+ANuxaWu72Pt4N5O3lqx0H11V4K7tlm+bJZDJpePdbbPZxJue4esddrdz8LKWk/loLRwg4Fz30AAAAAJzi4NHtkqSPlj6uj5Y+bnedU9mZ5W7v7eWjKVe/ojnfTNPEZ1opulmcurYdoj6dxqhb7GU2+7FYLLrt+Q7ltlXRfgCjINADAAAAcAqLxSJJGjfgPvVsf7nddQL9Qyps46red6pPp9Fat/07bd63Uj9v/lLfrHldg7pM0CM3zy/dkUwmk2bevkSeJk+77USHd6r+gQB1BIEeAAAAgFNEhsVKkjw8PNWt3dBqtxMaFKGRvf6skb3+rGJzsZ79dKJWJH2qcQPvU/uWPRUZFqv1O/+rpsFRim7WsabKB+ocxtADAAAAcIq2kYmKCe+sxWvnKv3EvjLLi4uLdDb3ZLnb5xXkKq8g1+Y5Tw9P6yz7pdsO7T5RkvT+kodVbC4u086pLC63h3ughx4AAACAU5hMJs24/j+a/uYQTX4pQZf3vE3RzTopvzBXh4/v0ZqtC3TbFc+UO8t92rFdum/uQPXtdI1ahXdWoH+IDmZu1+Jf5yi8cSvFt+ovSWrfsqcmDXtCH/3whO58uasGJIxXaFBznTybrt2HN+i3Hd9ryawCJx45UDsI9AAAAACcpm1kV825d5M+Xf6Mfk35VovXzlUD34YKD4nR8B5/UuJ5k9tdqGlwS13e8zYl7VmhX7YtVGFRvsIaReqKXndowuAZ8vPxt647cfjjateyh75e/Zq+/vkV5RXkKDiwqWLCO+uvo19zxqECtY5ADwAAAKBcXdoM0g/PW2ye+/jhAxfVZrOQaP3t2rmVrjdp+BOaNPwJ6+OggFBNufrlKu+nV8cr1avjldUpETAExtADAAAAAGBA9NADAAAAuCg5584ov/Bchet4efkoyL+xkyoC6gcCPQAAAICL8sY30/TDhg8rXCeh9UC9OOUn5xQE1BMEegAAAAAXZcLg6Rra7eYK1wn0D3FSNUD9QaAHAAAAcFGim8Upulmcq8sA6h0mxQMAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiFnuAQBwE0XFxbKYLXaWWKz/FhYW2Xn8B08vT3mYTLVYJQCgtpjNZhUXm+0sqfr7gIenhzw96Pc1CgI9AABu4kjGcc2Z960sFnuhXjqbnatHX3qv3MdRzZvqLzddLRHoAcCQCgqL9Or7X+nUmSy7yyt7H/Dx8dbfbr1WjYODar1W1Aw+egEAwE1ERTbT4Eu7Vmtbb28vXXflYHplAMDA/Hx9NP7KQarux7JXX9aHMG8wvGsDAOBGLuvbXZHNwhze7srBlyqscaNaqAgA4EytW0ao/yUJDm8XFxut7vHtaqEi1CYCPQAAbsTT00PXjRosLy/PKm/TvnVL9erasRarAgA40/D+PRXepHGV1w/0b6CxIwbIxJArwyHQuwGLxaJGjRrJZDLp2LFjDm07fvx4mUwmffDBB7VTHADA6ZqFhejygZdUaV3/Br669oqB/BEHAG7Ey8tTE0YNlqdn1eLe2CsGKDCgQS1XhdpAoHcDqampOnv2rMLDw9WkSROHtk1OTpYkde3atRYqAwC4Sp/undU2OrLS9a4Z0V9Bgf5OqAgA4EwRTUM1vH/PStfrkdBecW2jnVARagOB3g0cPHhQ7du314gRIxzaLicnR3v37pW3t7fi4uJqqToAgCt4mEwaN3Kg/Hx9yl2nW+dYxbdv7cSqAADO1L9nvFq1jCh3eeNGDXXVkN5OrAg1jUDvBgYMGKAdO3Y4fNn81q1bZTabFRcXJx+f8v/gAwAYU3BQoEYP61vusquH2l8G92c2m/XVqpd123MdNPIhP934r5aau+g+nSvIcXVpAGqQh4eHxl85SL4+3mWWmSRdN2qwfCv44Bd1H4G+HuNyewBwf13j2iqhg20vvEnS+CsHVdh7D/c2Z9G9mrvo74pqFqe7xszWgITxWrj6NT323lUym82uLg9ADWrcqKGuGtqnzPMDL+2qmBbhLqgINYlA70QxMTEymUzavn27fvnlF11zzTVq0qSJgoKCNHToUG3evNm67rp16zRu3Dg1a9ZMwcHBGjZsmLZt21Zhuzt27Ciz7MSJE5oxY4ZiY2Pl5+entm3b6vnnn5fFYrHur0uXLrVzwAAAlzOZTBozvJ8anjdOvm/PeLWJau7CquBKBzK26Zs1s9Wv81g9ccsCjex1h+68+iXdedVLStq7Qj8lz3d1iQBqWPfO7RQXG2N9HNE0VEP7dXddQagxBHonOX36tFJTU+Xr66vFixerf//+Wr9+vVq2bKmCggItW7ZMw4YN08mTJ/XCCy+ob9++Wrt2rSIjI5WXl6cff/xRw4YNU3Z2tt12/f391a6d7X0jk5KSFB8fr+eee05HjhxRXFycsrOzNX36dN1zzz300ANAPeHfwE/jrxgoqWQG/BEDKp8kCe5rRdKnslgsGtv/bzbPj+x1h/y8/fXjxo9dUxiAWmMymTT28v4K9G8gT08PTRg1WF6eVb+9KeouAr2TJCUlSZKKi4s1a9YsLViwQGlpadq4caO2bNmiRo0a6ejRo5o4caJmzpypL774wrp827ZtCgkJUXp6ur777ju77cbHx8vD448f57FjxzRq1Cilp6fr73//uzIyMrRx40ZlZGTomWee0RtvvKH169dLItADQH3QrnVL9e3RWRNGDZa3l5ery4EL7Ty0Xh4mD7WPsr21oY+3n1o376pdh9a7qDIAtSnQv4GuvWKARgy4xKF71KNu4x3dSUp7w4uLizV//nwNGzbMuiw2NlajR4/WRx99pCVLluj777/X5Zdfbl3epk0bXXXVVfroo4+0f/9+u+1eGMrvuusuHT58WHfffbdefPFFm2UPPvigPv/8c23atElRUVEKCQmpyUO1MfvDBcrKPldr7QMAHLNlx/7KV3IzWTm51n+ffmNemcfuxsPiqR66vtzlJ84eUVBAmHy8fMssC2sUqZTUX1RYVCBvr/o1x8Lzb82X2VTs6jJqRGXnAOxzp3OgMqvXb3F1CU5l73W/rr0XNAxsoLtvGevwdgR6JyntSZ8wYYJNmC/l7V0y8+S1115rE+ZLef1/b4q/v+29gkvbPX8c/NatW/XFF18oJCREM2fOtFvPJZdcok2bNpUZP79//35NmzZNK1askJeXl6666iq9/PLLCg0NrdqBXiAr+5zOZjNjLgDA9SwWi8170oWP3YWHvKQKrqTNL8iVt50wL0k+Xn4l6xTm1rtAfzYnV2YVubqMGlHZOQD73OkcgH32XveN/l5AoHeS0uB9/fX2Py1NSUmpcPnu3bslSe3bt7fb7vk99PPnz5fFYtENN9yghg0b2m2v9AOE87fLysrS4MGD1bhxY3366ac6d+6cpk+frlGjRmnNmjU2l/RXVcPABg5vAwBATcrKyZXFYpHJZFLDAP8yj92Nh8VTyit/ua+Pv85lH7W7rKCoZENfb/f7vlQmKMDfbXpnKzsHYJ87nQOwZe91v669F1Q3NxHonaCwsNAa2AcMGFBmudlsts44b2/5+TPSnx/AS9v18PBQQkKC9fkVK1ZIkoYOHVpuTfv27SvT3ltvvaXDhw9r1apVioqKkiS1aNFCffr00bfffqsxY8ZU4WhtVeeyEQAAatLTb8zT2ewcNQzw18NTbyrz2N0UF0grXit/eWhQcx3MTFFBUX6Zy+6PnzmsRgFh9a53XpIemHy9PN3ksCs7B2CfO50DsGXvdd9d3gtMFovF4uoi3N3mzZvVpUsXRUdH68CBA2WW79y5Ux06dFBkZKTS0tLKLN+3b5/atGmj8PBwpaenl2k3NjZWu3btsj4fFRWlQ4cOadOmTXYnvMvLy1OzZs109uxZ7d27V61bl9yfePDgwZL++ECgVJs2bTRo0CC9++67Dh87Y+gBAK5WH3voe+SVP376/f/+Q58sm6mXpqxSfOv+1ucLCvN07eOhim89QE//eYkzSq1Tfvdzn/HTlZ0DsM+dzgHYMkoPPWPo66jSy+ITExMvavmF4by857OysiRJOTn2x4LMmzdPZ8+eVVBQkFq1amV9PiUlRePHjy+zfqdOnaxXGDiKMfQAgLqCMfQlBnWZoE+XP60FP79iE+i/X/e28gpzNSTRuD1VF8Odxk8zhr563OkcgH2MoUe1lM5EX91AX95M9uU9HxERodOnT2vt2rXq27evzbLMzEw9+uijkqSEhASZTCbrslOnTik4OLjM/hs3bqydO3fara0yjKEHALhafeyhr2j8dKuIeF3dZ6q+WfO6nvhwrC7pMFIHj27XwtWvKaH1QA1JvNF5xdYh7jR+mjH01eNO5wBsGaWHvjoI9E5QWWDftGlThcsr66G/cKb6oUOHavv27Xr22Wc1ZMgQa7vJycm6+eabrZftO+P+84yhBwC4GmPoy5py9StqFhKj79e9pd+2f6eggDCN6Xu3bhnxz2pNgusO3Gn8NGPoq8edzgHYcucx9AR6J7jYHvryAn15PfTTp0/XJ598omPHjqlHjx5q3769zGazdu7cqWbNmql379769ddfy2wXEhKi06dPl9n/yZMn1bhx4/IPEAAAGIqnh6fGD7xP4wfe5+pSAAAXoX5+BOtEaWlpOnHihMLCwtSiRYsyyzMyMpSZmang4GDFxMSUWX769GkdPHhQ/v7+io2NtdtuZGSkzTYtWrTQ6tWrNWrUKAUEBOjAgQOyWCyaNm2akpKSrGPsLwz0HTt2tDtWPiUlRR07dqzG0QMAAAAAags99LWsRYsWquhGAuHh4RUuDw4Otru8snY7dOigRYsW2V22ZcsWu8+PGjVKDz/8sNLS0qwfPqxbt0579+7V888/X+6+AAAAAADORw89rCZPnqyIiAiNHj1aixcv1pdffqkbbrhBl1xyiUaPHu3q8gAAAAAA5yHQwyooKEjLly9XRESErr/+ev35z39Wnz59tHjx4no7QQ4AAAAA1FVccg8bbdq00eLFi11dBgAAAACgEnS7AgAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABebm6AAAAANQcD29p8D2ursJ4PLxdXQEAOI5ADwAA4EZMJsnTx9VVAACcgUvuAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAARHoAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAARHoAQAAAAAwIAI9AAAAAAAGRKAHAAAAAMCACPQAAAAAABgQgR4AAAAAAAMi0AMAAAAAYEAEegAAAAAADIhADwAAAACAARHoAQAAAAAwIC9XFwAAAFATCouK9M0Pa1RQUGjz/Lm8POu/n3zzY5nH5+t/SYJaRjR1TsEAgBq36rfNSks/avOcvdf98t4L2kZH6pKuHZ1U7cUj0AMAALfg7eWlqIimWvC/n+0uLywq1uYd+8p93DY6UpHhTWq9TgBA7enYJko//LxehUXFZZZd+Lp/4XP+fr4aNaS3U+qsKVxyDwAA3EbPLh3UoU2Uw9v5+fpo3MiB8jCZaqEqAICzNAkN1sjBl1Zr2zEj+iuoYUANV1S7CPQAAMBtmEwmXXv5AAU08HNou9HD+io4KLCWqgIAONOliXGKjWnh0DaJndoqoUPrWqqo9hDo3YDFYlGjRo1kMpl07Ngxh7YdP368TCaTPvjgg9opDgAAJ2sY6K9rLu9f5fUTOrRW17i2tVgRAMCZTCaTxo0cqAZ+vlVav1HDAF09tG8tV1U7CPRuIDU1VWfPnlV4eLiaNHFs7F9ycrIkqWvXrrVQGQAArtG5XSt179yu0vUaBvprzPB+MnGpPQC4lUYNAzRmeL8qrTv+ykFVDv91DYHeDRw8eFDt27fXiBEjHNouJydHe/fulbe3t+Li4mqpOgAAXOOqoX0qvYx+/BUD5e/g5fkAAGPo0rFNpVdg9e3RWW2jI51UUc1jlns3MGDAAO3YscPh7bZu3Sqz2az4+Hj5+PjUQmUAALiOn6+PrrtykN7+dLEsdpZfmhindq1bOr0uoC4wm836evWr+m7tm8o4dUDBAU00oMt1umXEP9XAx1iTggEVuXpYX+07mK6z2TllljUNDdHlAy5xQVU1hx76eozL7QEA7q51VHP1vyShzPNhjRtp5KBeLqgIqBvmLLpXcxf9XVHN4nTXmNkakDBeC1e/psfeu0pms9nV5QE1xt/PV+OvHFjmeQ8PkyZcNVje3sbu4ybQO1FMTIxMJpO2b9+uX375Rddcc42aNGmioKAgDR06VJs3b7auu27dOo0bN07NmjVTcHCwhg0bpm3btlXYrr1e+hMnTmjGjBmKjY2Vn5+f2rZtq+eff14Wi8W6vy5dutTOAQMAUAcM799T4U0aWx97mEyacOVg+fh4u7AqwHUOZGzTN2tmq1/nsXrilgUa2esO3Xn1S7rzqpeUtHeFfkqe7+oSgRoVG9NCfbp3tnluWL8eimwW5qKKag6B3klOnz6t1NRU+fr6avHixerfv7/Wr1+vli1bqqCgQMuWLdOwYcN08uRJvfDCC+rbt6/Wrl2ryMhI5eXl6ccff9SwYcOUnZ1tt11/f3+1a2c7+U9SUpLi4+P13HPP6ciRI4qLi1N2dramT5+ue+65hx56AEC94OXlqetGDZanR8mfPYP7JKpl86YurgpwnRVJn8pisWhs/7/ZPD+y1x3y8/bXjxs/dk1hQC26YuAlatI4WJIU1byZBvRyj05NAr2TJCUlSZKKi4s1a9YsLViwQGlpadq4caO2bNmiRo0a6ejRo5o4caJmzpypL774wrp827ZtCgkJUXp6ur777ju77cbHx8vD448f57FjxzRq1Cilp6fr73//uzIyMrRx40ZlZGTomWee0RtvvKH169dLItADANxf86ahGj6gh1qEN9GQ3t1cXQ7gUjsPrZeHyUPto2zHDvt4+6l1867adWi9iyoDao+3t5cmXDW4ZH6VUYOsH/IanbEHDBhIaW94cXGx5s+fr2HDhlmXxcbGavTo0froo4+0ZMkSff/997r88suty9u0aaOrrrpKH330kfbv32+33QtD+V133aXDhw/r7rvv1osvvmiz7MEHH9Tnn3+uTZs2KSoqSiEhITV5qDZmf7hAWdnnaq19AACqziKLxaJn537q6kKAWuVh8VQPXV/u8hNnjygoIEw+XmVv0xXWKFIpqb+osKhA3l71a9Lk59+aL7Op2NVloJZ5eXnqrU8Wu7qMMhoGNtDdt4x1eDsCvZOU9qRPmDDBJsyX8vYuGcd37bXX2oT5Ul5eJT8qf39/u+2ePw5+69at+uKLLxQSEqKZM2fareeSSy7Rpk2bbLZLS0vTrFmz9Ntvvyk5OVkFBQWyWOzNC1x1Wdnn7M4oCQAAgNrhIS/Js/zl+QW58rYT5iXJx6vkNo75hbn1LtCfzcmVWUWuLgNwCIHeSUqD9/XX2/+0NCUlpcLlu3fvliS1b9/ebrvn99DPnz9fFotFN9xwgxo2bGi3vdIPEM7fbs+ePfrqq6/Us2dP+fj4aM2aNRUeU1U0DGxw0W0AAACg6jwsnlJe+ct9ffx1Lvuo3WUFRSUb+nr7213uzoIC/Omhh8tUNzcR6J2gsLDQGtgHDBhQZrnZbLbOOG9v+fkz0p8fwEvb9fDwUELCH7fkWbFihSRp6NCh5da0b9++Mu0NGDBA6enpkqQnnniiRgJ9dS4bAQAAQPUVF0grXit/eWhQcx3MTFFBUX6Zy+6PnzmsRgFh9a53XpIemHy9POvfYcPgCPROsH37dhUUFCg6OtruePXdu3crJydHkZGRatKkSZnl+/fv15kzZxQeHq5mzZqVaTc2NlYBAQHW5w8dOiRJatWqld168vLytHr1akm2gd6jFiaGYAw9AACAc1U2hr59y57asGupdh78TfGt+1ufLyjM074jSYpvXbaDqT5gDD1ciTH0dVjpZfGJiYkXtfzCie/Kez4rK0uSlJNjf+z6vHnzdPbsWQUFBZUb+msKY+gBAACcq7Ix9IO6TNCny5/Wgp9fsQn03697W3mFuRqSeJMTqqx7GEMPIyLQO0HpTPTVDfTlzWRf3vMRERE6ffq01q5dq759+9osy8zM1KOPPipJSkhIkMlkqvJxVAdj6AEAAJyrsjH0rSLidXWfqfpmzet64sOxuqTDSB08ul0LV7+mhNYDNSTxRucVW4cwhh6uxBj6OqyywL5p06YKl1fWQ3/+TPVSydj57du369lnn9WQIUOs7SYnJ+vmm2+2jpN3xv3nGUMPAADgXJWNoZekKVe/omYhMfp+3Vv6bft3CgoI05i+d+uWEf+slWGYRsAYehgRgd4JLraHvrxAX14P/fTp0/XJJ5/o2LFj6tGjh9q3by+z2aydO3eqWbNm6t27t3799VenBHoAAADUPZ4enho/8D6NH3ifq0sBcBHq58dvTpSWlqYTJ04oLCxMLVq0KLM8IyNDmZmZCg4OVkxMTJnlp0+f1sGDB+Xv76/Y2Fi77UZGRtps06JFC61evVqjRo1SQECADhw4IIvFomnTpikpKck6xp5ADwAAAADGRQ99LWvRooUsFku5y8PDwytcHhwcbHd5Ze126NBBixYtsrtsy5YtFVQMAAAAADACAj1sfPnll5KklJQUm8cxMTHq0aOHy+oCAAAAANgyWSrq5kW9U96s97fccos++OAD5xYDAAAAh1VlUjyUNfgeMSkeDIceetjg8x0AAAAAMAYmxQMAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAzJZLBaLq4sAAAAAUDMsFslc6OoqjMfDWzKZXF0F4BgCPQAAAAAABsQl9wAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEegBAAAAADAgAj0AAAAAAAZEoAcAAAAAwIAI9AAAAAAAGBCBHgAAAAAAAyLQAwAAAABgQAR6AAAAAAAMiEAPAAAAAIABEehdLD09XZMnT1ZMTIx8fHxkMpk0YsQINWrUSCaTSceOHXNpfRaLRUFBQTKZTDp+/LhLawEAAAAA/MHL1QXUZzk5Oerfv7/27t2rxo0bq1u3bvLy8tLIkSO1dOlShYeHq0mTJi6tce/evcrKylJkZKTCwsJcWgsAAAAA4A8Eehd68803tXfvXg0aNEhLliyRn5+fJGnVqlVq3769Lr30UhdXKCUnJ0uSunbt6tpCAAAAAAA2CPQu9PXXX0uS/va3v1nDvCQNGDBAO3bscFVZNpKSkiRJXbp0cW0hAAAAAAAbjKF3EbPZrA0bNkiS+vbt6+JqykcPPQAAAADUTQR6J8vIyJDJZJKnp6fOnTsnSWrSpIlMJpNMJpNWrVqlmJgYmUwmu7304eHhMplMOnDggJKSkjRx4kRFRkYqICBAXbt21fz58+3u98CBA3r++ed1+eWXq02bNgoICJC/v7+6dOmiWbNmqaCgwO529NADAAAAQN1kslgsFlcXUZ9s3rxZf/3rX3X06FHt3r1bTZo0Ubt27azLv/32W4WGhsrf319ZWVny8PjjM5fMzEyFh4crKChITz75pO6//36FhoYqKipK+/bt08mTJyVJ//vf/zR8+HCb/d5+++167733FBAQoObNmys4OFhHjx5VamqqJGns2LH66quvbLY5deqUGjdurICAAJ09e9amFgAAAACAa5HQnCwhIUGrV6/WjTfeKEm67bbbtHr1auvX5s2bJUnx8fFlAnTp5e95eXl68sknNW/ePGVkZGj9+vU6fPiwRo0aJUl69913y+x3+PDh2rBhg7KysrRr1y799ttvOnDggNavX69mzZppwYIF2rJli802pb3z9moBAAAAALgWk+K5SHmXslc0Zr10WWFhoT755BNdccUV1mV+fn6aMmWKFi9ebO11P9+ECRPs1tGjRw8NGzZMH3/8sbZt26b4+Pgq1VJVsz9coKzsc9XeHgAAAADcXcPABrr7lrEOb0egd5HSQH9hWK5ozHppwB43bpxNmC9VOg4+ODi4zLL8/Hx9++23Wrlypfbv36+srCyZzWZJ0rZt2yRJPj4+Va6lqrKyz+lsdk61twcAAAAA2Eegd4HTp08rNTVVDRo0sBk/L5Uf9KU/An15ve07d+6UpDJtLlu2TLfeeqsOHTpUYV2tW7euci1V1TCwQbW3BQAAAID6oLq5iUDvAuePTff09LQ+X1hYqJSUFHl4eCghIcFmm/z8fOus94MGDaqw3fMD+Lp16zRy5EgVFhbq9ttv10033aTOnTsrJCREXl5e2rFjhzp27CgvLy917NjRppbt27fbrcUR1blsBAAAAABQOQK9C5R3Kfv27dtVUFCg2NhYBQQE2CxLSUlRUVGRoqKiFBoaarfdTZs2SbIN9A899JAKCgo0a9YszZgxo8w23333nSQpLi5Ovr6+NvsrKChQu3bt5O/v7/AxlmIMPQAAAABUjDH0BlLZ+PmKLrdPTEy022ZOTo52794tb29vde7cWZJUXFysVatWSZJuvfXWMtuYzWbNmzfPbrs1MSGexBh6AAAAAKgtBHoXKC+4V2WG+/IC/ebNm2U2m9W5c2fr5HY5OTkqLi6WVHKruwu9+OKL1l79C9utiQnxJMbQAwAAAEBlGENvEAUFBUpJSZHJZCozNr0qM9yXF+jtBfOgoCBFRkbq8OHDmjlzpv7973/L09NTBQUFevXVV/Xoo4/Ky8tLRUVFtdZDzxh6AAAAAKgdHq4uoL5JSUlRYWGh2rRpo8DAQJtlF9NDb2/8vCQ98sgjkqS33npLkZGR6tmzp5o2bapHHnlEs2fPlsVikclkKvdqgYvtoQcAAAAA1A566J2svMvt09LSdOLECYWFhSkyMrLMspMnTyo0NFQtW7a02255gX7KlCny8vLS888/rwMHDsjT01OjRo3SjBkzZDabVVxcrDZt2igoKKhKtQAAAAAA6gaTxWKxuLoIAAAAAADgGC65BwAAAADAgAj0AAAAAAAYEIEeAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCACPQAAAAAABkSgBwAAAADAgAj0AAAAAAAYEIEeAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCACPQAAAAAABkSgBwAAAADAgAj0AAAAAAAYEIEeAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCACPQAAAAAABkSgBwAAAADAgAj0AAAAAAAYEIEeAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCACPQAAAAAABkSgBwAAAADAgAj0AAAAAAAYEIEeAAAAAAADItADAAAAAGBABHoAAAAAAAyIQA8AAAAAgAER6AEAAAAAMCACPQAAAAAABkSgBwAAAADAgP4Pw+5VEJAGKpAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1289.54x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reset_controls(\n",
    "    circuit: QuantumCircuit, controls: QuantumRegister, measures: ClassicalRegister\n",
    "):\n",
    "    \"\"\"Reset the control qubits if they are in |1>.\"\"\"\n",
    "\n",
    "    ####### your code goes here #######\n",
    "    with circuit.if_test((measures[0], 1)):\n",
    "        circuit.x(controls[0])\n",
    "    with circuit.if_test((measures[1], 1)):\n",
    "        circuit.x(controls[1])\n",
    "  \n",
    "    \n",
    "\n",
    "\n",
    "qc = base.copy_empty_like()\n",
    "trial(qc, target, controls, mid_measure)\n",
    "reset_controls(qc, controls, mid_measure)\n",
    "qc.measure(controls, mid_measure)\n",
    "qc.draw(\"mpl\", cregbundle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e6443405",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "from qc_grader.challenges.spring_2023 import grade_ex1d\n",
    "\n",
    "grade_ex1d(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2c844e",
   "metadata": {},
   "source": [
    "Now all that is left to do is repeat the execution of the circuit if the conditions weren't satisfied.\n",
    "In the final exercise, we will need to work around two issues in our current support for dynamic circuits.\n",
    "\n",
    "The first issue is that Qiskit currently does not support performing any arithmetic or logical operations on classical bits. In particular, it does not support checking that a classical register does *not* have a certain value. In our situation, we need to repeat the trial only if the mid-circuit measurement was *not* the bitstring `00`. To work around this issue, we will create an if statement conditioned on the measurement being equal to `00`, **pass** an empty block, and then use the **else** branch to perform the logic we want for the case that the measurement is *not* `00`.\n",
    "\n",
    "The second issue is that our hardware currently does not support loops. Therefore, we cannot execute a true repeat-until-success loop. We will work around this issue by simply repeating our trial circuit a fixed number of times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3c5b4e",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "\n",
    "In the code cell below, fill in the `else` block with the logic that should be performed in case the syndrome measurement indicates we need to repeat the trial:\n",
    "\n",
    "1. Reset the target qubit to the zero state. Remember, we already know that it is in the 1 state.\n",
    "2. Call the `trial` function on the circuit again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4be1b959",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1540.38x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the maximum number of trials\n",
    "max_trials = 2\n",
    "\n",
    "# Create a clean circuit with the same structure (bits, registers, etc)\n",
    "# as the initial base we set up.\n",
    "circuit = base.copy_empty_like()\n",
    "\n",
    "# The first trial does not need to reset its inputs, since the controls\n",
    "# are guaranteed to start in the |0> state.\n",
    "trial(circuit, target, controls, mid_measure)\n",
    "\n",
    "# Manually add the rest of the trials.  In the future, we will be\n",
    "# able to use a dynamic `while` loop to do this, but for now, we\n",
    "# statically add each loop iteration with a manual condition check\n",
    "# on each one.  This involves more classical synchronizations than\n",
    "# the while loop, but will suffice for now.\n",
    "for _ in range(max_trials - 1):\n",
    "    reset_controls(circuit, controls, mid_measure)\n",
    "    with circuit.if_test((mid_measure, 0b00)) as else_:\n",
    "        # This is the success path, but Qiskit can't directly\n",
    "        # represent a negative condition yet, so we have an\n",
    "        # empty `true` block in order to use the `else` branch.\n",
    "        pass\n",
    "    with else_:\n",
    "        ####### your code goes here #######\n",
    "        circuit.reset(2)\n",
    "        trial(circuit, target, controls, mid_measure)\n",
    "\n",
    "\n",
    "# We need to measure the control qubits again to ensure we\n",
    "# get their final results; this is a hardware limitation.\n",
    "circuit.measure(controls, mid_measure)\n",
    "\n",
    "# Finally, let's measure our target, to check that we're\n",
    "# getting the rotation we desired.\n",
    "circuit.measure(target, final_measure)\n",
    "\n",
    "circuit.draw(\"mpl\", cregbundle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "973b441d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "from qc_grader.challenges.spring_2023 import grade_ex1e\n",
    "\n",
    "grade_ex1e(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f84bb06",
   "metadata": {},
   "source": [
    "Let's run the circuit on a simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f576413c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = AerSimulator()\n",
    "job = sim.run(circuit, shots=1000)\n",
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1971f98e",
   "metadata": {},
   "source": [
    "A successful result is one in which the measurements on the two controls end in the `00` state. If you're having trouble, we encourage you to post about your results on the Discord Channel, and try to understand in collaboration with other participants what your results mean and why you are getting them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cb07ef",
   "metadata": {},
   "source": [
    "## Run on hardware\n",
    "\n",
    "Let's try running the circuit we made on real hardware! For this, we'll be using the 27-qubit Peekskill device, which has been tuned specifically for dynamic circuits. But please remember, running on real hardware takes time, and sometimes has errors. Please **do not** run these cells over and over, since that will cause a backup for all the other Challenge participants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f917e10b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_ibm_provider import IBMProvider\n",
    "\n",
    "provider = IBMProvider()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "691dd8d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hub = \"qc-spring-23-8\"\n",
    "group = \"group-1\"\n",
    "project = \"recSGJPq7n3GSkwAb\"\n",
    "\n",
    "backend_name = \"ibm_peekskill\"\n",
    "backend = provider.get_backend(backend_name, instance=f\"{hub}/{group}/{project}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "57735462",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit import transpile\n",
    "\n",
    "qc_transpiled = transpile(circuit, backend)\n",
    "job = backend.run(qc_transpiled, shots=1000, dynamic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7ee1c832",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "counts = job.result().get_counts()\n",
    "\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6acb6b58",
   "metadata": {},
   "source": [
    "## Congratulations!\n",
    "\n",
    "You've made it to the end of the first lab! Now that you know more about dynamic circuits, it is time to move onto the next lab and start learning about some applications and more advanced properties. Good luck!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}