qaoa_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e544bf6e-d329-4ffa-af5e-bc09c6754c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting qiskit-nature\n",
      "  Using cached qiskit_nature-0.7.2-py3-none-any.whl.metadata (8.0 kB)\n",
      "Requirement already satisfied: qiskit>=0.44 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (1.3.1)\n",
      "Collecting qiskit-algorithms>=0.2.1 (from qiskit-nature)\n",
      "  Using cached qiskit_algorithms-0.3.1-py3-none-any.whl.metadata (4.2 kB)\n",
      "Requirement already satisfied: scipy>=1.4 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (1.14.1)\n",
      "Requirement already satisfied: numpy>=1.17 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (1.26.4)\n",
      "Requirement already satisfied: psutil>=5 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (6.1.1)\n",
      "Requirement already satisfied: setuptools>=40.1.0 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (75.1.0)\n",
      "Requirement already satisfied: typing-extensions in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (4.12.2)\n",
      "Collecting h5py (from qiskit-nature)\n",
      "  Using cached h5py-3.12.1-cp310-cp310-macosx_11_0_arm64.whl.metadata (2.5 kB)\n",
      "Requirement already satisfied: rustworkx>=0.12 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit-nature) (0.15.1)\n",
      "Requirement already satisfied: sympy>=1.3 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit>=0.44->qiskit-nature) (1.13.3)\n",
      "Requirement already satisfied: dill>=0.3 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit>=0.44->qiskit-nature) (0.3.9)\n",
      "Requirement already satisfied: python-dateutil>=2.8.0 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit>=0.44->qiskit-nature) (2.9.0.post0)\n",
      "Requirement already satisfied: stevedore>=3.0.0 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit>=0.44->qiskit-nature) (5.4.0)\n",
      "Requirement already satisfied: symengine<0.14,>=0.11 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from qiskit>=0.44->qiskit-nature) (0.13.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from python-dateutil>=2.8.0->qiskit>=0.44->qiskit-nature) (1.16.0)\n",
      "Requirement already satisfied: pbr>=2.0.0 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from stevedore>=3.0.0->qiskit>=0.44->qiskit-nature) (6.1.0)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/alexanderpatrie/miniforge3/envs/server/lib/python3.10/site-packages (from sympy>=1.3->qiskit>=0.44->qiskit-nature) (1.3.0)\n",
      "Using cached qiskit_nature-0.7.2-py3-none-any.whl (2.2 MB)\n",
      "Using cached qiskit_algorithms-0.3.1-py3-none-any.whl (310 kB)\n",
      "Using cached h5py-3.12.1-cp310-cp310-macosx_11_0_arm64.whl (2.9 MB)\n",
      "Installing collected packages: h5py, qiskit-algorithms, qiskit-nature\n",
      "Successfully installed h5py-3.12.1 qiskit-algorithms-0.3.1 qiskit-nature-0.7.2\n"
     ]
    }
   ],
   "source": [
    "!pip install qiskit-nature\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca6612cb-4390-44ab-adb2-52347b0f0e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi\n",
    "\n",
    "import numpy as np\n",
    "import rustworkx as rx\n",
    "from qiskit_nature.second_q.hamiltonians.lattices import (\n",
    "    BoundaryCondition,\n",
    "    HyperCubicLattice,\n",
    "    Lattice,\n",
    "    LatticeDrawStyle,\n",
    "    LineLattice,\n",
    "    SquareLattice,\n",
    "    TriangularLattice,\n",
    ")\n",
    "from qiskit_nature.second_q.hamiltonians import FermiHubbardModel\n",
    "from process_bigraph import Process, Composite, Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d96bec83-feb3-4e0d-90d7-371fa0a043ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3857580-7117-41a8-b58e-3460e3ca3824",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23d005d8-a900-4154-8a8c-a31ffebd73e2",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after function definition on line 14 (3854564678.py, line 17)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[11], line 17\u001b[0;36m\u001b[0m\n\u001b[0;31m    class QAOA(Process):\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after function definition on line 14\n"
     ]
    }
   ],
   "source": [
    "from bio_bundles import registrar\n",
    "from bio_bundles.data_model import BaseClass\n",
    "\n",
    "from typing import *\n",
    "\n",
    "import networkx as nx\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "\n",
    "\n",
    "CORE = registrar.core\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class Problem(BaseClass):\n",
    "    pass\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class NVariableProblem(Problem):\n",
    "    n_vars: int\n",
    "    inputs: Optional[Dict] = None\n",
    "\n",
    "    def __post_init__(self):\n",
    "        self.inputs = self.get_input_data()\n",
    "\n",
    "    def get_input_data(self, *args, **kwargs):\n",
    "        # TODO: perform some logic here to format Nvar problem\n",
    "        return {}\n",
    "\n",
    "    \n",
    "class QAOA(Process):\n",
    "    # same here (format n var prob)\n",
    "    config_schema = {\n",
    "        'num_nodes': 'integer',\n",
    "        'max_depth': 'integer'\n",
    "    }\n",
    "\n",
    "    def __init__(self, config, core):\n",
    "        # TODO: make n_vars mapped more to real\n",
    "        super().__init__(config, core)\n",
    "\n",
    "        # TODO: infer this from the given composite.json file as input.\n",
    "        self.max_depth = self.config.get(\"max_depth\", 3)\n",
    "        self.N = self.config[\"num_nodes\"]\n",
    "        self.graph = nx.complete_graph(self.N)\n",
    "\n",
    "        # define optimizer\n",
    "        self.optimizer = COBYLA()\n",
    "\n",
    "    def initial_state(self):\n",
    "        # get initial problem hamiltonian from weights set in init\n",
    "        initial_h_problem = self._get_problem_hamiltonian()\n",
    "        \n",
    "        # get mixer hamiltonian from current N(self.N)\n",
    "        initial_h_mixer = self._get_mixer_hamiltonian(self.N)\n",
    "        \n",
    "        # get intitial ansatz\n",
    "        initial_ansatz = self._get_ansatz(self.N)\n",
    "\n",
    "        return {\n",
    "            \"H_P\": initial_h_problem,\n",
    "            \"H_M\": initial_h_mixer,\n",
    "            \"ansatz\" initial_ansatz\n",
    "        }\n",
    "        \n",
    "    def inputs(self):\n",
    "        return {\n",
    "            \"H_P\":{}}\n",
    "\n",
    "    def outputs(self):\n",
    "        return {}\n",
    "\n",
    "    def _get_problem_hamiltonian(self):\n",
    "        h = 0\n",
    "        for i, j in self.graph.edges():\n",
    "            weight = self.graph[i][j]['weight']\n",
    "            h += weight * (0.5 * (1 - Z(i) @ Z(j)))\n",
    "        return h\n",
    "\n",
    "    def _get_mixer_hamiltonian(self, N: int):\n",
    "        return sum([X(i) for i in range(N)])\n",
    "\n",
    "    def _get_ansatz(self, N: int):\n",
    "        return TwoLocal(N, 'ry', 'cz', reps=self.max_depth, entanglement='linear')\n",
    "        \n",
    "    def _compute_edge_weight(a, b):\n",
    "        # TODO: implement this, for now returing random\n",
    "        return np.random.rand()\n",
    "\n",
    "    def check_n(self):\n",
    "        pass\n",
    "\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6fb26dcb-3ffb-4114-9a37-581013829fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.complete_graph(4)\n",
    "G.edges()\n",
    "for u, v in G.edges():\n",
    "    edge = G[u][v]\n",
    "    edge['weight'] = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e3fe572-6a4d-462f-abc0-b16927afaa86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x33cb64580>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ffaac0c2-fecf-4196-9926-6c1877b86a19",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'qiskit.opflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mqiskit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Z, I\n\u001b[1;32m      2\u001b[0m H_P \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m([w_ij \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m0.5\u001b[39m \u001b[38;5;241m*\u001b[39m (I \u001b[38;5;241m-\u001b[39m Z(i) \u001b[38;5;241m@\u001b[39m Z(j))) \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m graph\u001b[38;5;241m.\u001b[39medges()])\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'qiskit.opflow'"
     ]
    }
   ],
   "source": [
    "from qiskit.opflow import Z, I\n",
    "H_P = sum([w_ij * (0.5 * (I - Z(i) @ Z(j))) for i, j in graph.edges()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d755920-e6f1-4add-9551-53d633904e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import ZGate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b74068b-b7d8-412c-aedf-8f1a71799169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rustworkx.NodeIndices at 0x33cb5c280>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rustworkx as rx\n",
    "from rustworkx.visualization import mpl_draw as draw_graph\n",
    "N = 5\n",
    "graph = rx.PyGraph()\n",
    "graph.add_nodes_from(np.arange(0, N, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5fbb3406-9844-4dc1-a36f-35aafc1da17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_list = [(0, 1, 1.0), (0, 2, 1.0), (0, 4, 1.0), (1, 2, 1.0), (2, 3, 1.0), (3, 4, 0.0)]\n",
    "graph.add_edges_from(edge_list)\n",
    "draw_graph(graph, node_size=600, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "391d4d7f-b537-43e2-ad64-93ec7a3792b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "BioCompose Server(server): Python 3.10.16",
   "language": "python",
   "name": "server"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}