{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Scipy minimize variational plugin\n",
        "\n",
        "In this notebook, we briefly introduce the particularization of the `Optimizer` abstract plugin for `scipy.optimize.minimize`.\n",
        "\n",
        "We assume that you are already familiar with the `Optimize` class. If not, you can access a detailed notebook introducting this class [here](../plugins/junctions_and_optimizers.ipynb).\n",
        "\n",
        "`ScipyMinimizePlugin` is an `Optimizer` wrapping the `scipy.optimize.minimize` method, thus inheriting from all the underlying minimization algorithms.\n",
        "\n",
        "The plugin can be instantiated as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy\n",
        "from qat.vsolve.optimize import ScipyMinimizePlugin\n",
        "from scipy.optimize import minimize\n",
        "\n",
        "## A cobyla minimizer over any number of variables, random initialization, 20 max steps\n",
        "cobyla = ScipyMinimizePlugin(tol=1e-2, \n",
        "                             method=\"COBYLA\", \n",
        "                             options={\"maxiter\": 20})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lets try to use this plugin to solve a QAOA instance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from qat.opt import MaxCut\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "from qat.qpus import get_default_qpu\n",
        "\n",
        "qpu = get_default_qpu()\n",
        "\n",
        "stack = cobyla | qpu\n",
        "\n",
        "graph = nx.cycle_graph(4)\n",
        "\n",
        "problem = MaxCut(graph)\n",
        "job = problem.to_job(\"qaoa\", 2)  # '2' is the depth\n",
        "circuit = job.circuit\n",
        "result = stack.submit(job)\n",
        "print(\"The maxcut problem:\")\n",
        "print(problem)\n",
        "print(\"Final energy:\", result.value)\n",
        "print(\"The optimization data:\")\n",
        "print(result.meta_data[\"optimizer_data\"])\n",
        "print(\"The best set of parameters:\")\n",
        "print(result.meta_data[\"parameters\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Notice that the 'optimizer_data' entry of the result's meta_data contains the (stringified) output of scipy's minimize function.\n",
        "\n",
        "As we can see, 20 iterations are not enough for the optimizer to converge.\n",
        "Lets try with 200:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cobyla = ScipyMinimizePlugin(method=\"COBYLA\", \n",
        "                             tol=1e-2, \n",
        "                             options={\"maxiter\": 200})\n",
        "stack = cobyla | qpu\n",
        "result = stack.submit(job)\n",
        "print(\"The maxcut problem:\")\n",
        "print(problem)\n",
        "print(\"Final energy:\", result.value)\n",
        "print(\"The optimization data:\")\n",
        "print(result.meta_data[\"optimizer_data\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      "Simon Martiel",
      "Thomas Ayral"
    ],
    "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": "3.12.11"
    },
    "tags": [
      "variational"
    ]
  },
  "nbformat": 4,
  "nbformat_minor": 2
}