15 - pegasosqsvc_and_qsvc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import Aer\n",
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "from qiskit.utils import algorithm_globals, QuantumInstance\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "from qiskit_machine_learning.algorithms import PegasosQSVC, QSVC\n",
    "from qiskit_machine_learning.kernels import QuantumKernel\n",
    "\n",
    "algorithm_globals.random_seed = 10598"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of qubits is equal to the number of features\n",
    "num_qubits = 4\n",
    "\n",
    "# number of steps performed during the training procedure\n",
    "tau = 100\n",
    "\n",
    "# regularization parameter\n",
    "C = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PegasosQSVC classification test score: 0.8\n"
     ]
    }
   ],
   "source": [
    "pegasos_backend = QuantumInstance(\n",
    "    Aer.get_backend(\"statevector_simulator\"),\n",
    "    seed_simulator=algorithm_globals.random_seed,\n",
    "    seed_transpiler=algorithm_globals.random_seed,\n",
    ")\n",
    "\n",
    "features, labels = make_blobs(n_samples=20, n_features = 4, centers = 2, random_state = 3, shuffle = True)\n",
    "features = MinMaxScaler(feature_range=(0, np.pi)).fit_transform(features)\n",
    "\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, train_size=15, shuffle=False)\n",
    "\n",
    "feature_map = ZZFeatureMap(feature_dimension = num_qubits, reps=1)\n",
    "qkernel = QuantumKernel(feature_map = feature_map, quantum_instance=pegasos_backend)\n",
    "\n",
    "pegasos_qsvc = PegasosQSVC(quantum_kernel = qkernel, C = C, num_steps = tau)\n",
    "pegasos_qsvc.seed = 7\n",
    "\n",
    "# training\n",
    "pegasos_qsvc.fit(train_features, train_labels)\n",
    "\n",
    "# testing\n",
    "pegasos_score = pegasos_qsvc.score(test_features, test_labels)\n",
    "print(f\"PegasosQSVC classification test score: {pegasos_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSVC classification test score: 0.8\n"
     ]
    }
   ],
   "source": [
    "qsvc = QSVC(quantum_kernel = qkernel)\n",
    "\n",
    "# training\n",
    "qsvc.fit(train_features, train_labels)\n",
    "\n",
    "# testing\n",
    "qsvc = qsvc.score(test_features, test_labels)\n",
    "print(f\"QSVC classification test score: {qsvc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import qiskit_machine_learning\n",
    "qiskit_machine_learning.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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