Jamal_QNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c756835b-66c5-4bb7-a684-7cfcd5865fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection, datasets, svm\n",
    "from qiskit import QuantumCircuit, Aer, IBMQ, QuantumRegister, ClassicalRegister\n",
    "import qiskit\n",
    "import numpy as np\n",
    "import copy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99000a31-3880-4a97-9b21-b999cfec8232",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris.data[0:100]\n",
    "Y = iris.target[0:100]\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, train_size=0.8, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "637fe353-ee6c-4e4f-9be2-c51a8a78fefe",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c5fe7ee-a0f8-4643-9e0a-944422a13570",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_map(X) :\n",
    "    q = QuantumRegister(N)\n",
    "    c = ClassicalRegister(1)\n",
    "    qc = QuantumCircuit(q,c)\n",
    "    \n",
    "    for i, x in enumerate(X) :\n",
    "        qc.rx(x, i)\n",
    "        \n",
    "    return qc, c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2a4216a-be26-44b5-97ba-344cfe708c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def variational_circuit(qc, theta):\n",
    "    \n",
    "    for i in range(N-1):\n",
    "        qc.cnot(i, i+1)\n",
    "    qc.cnot(N-1, 0)\n",
    "    for i in range(N):\n",
    "        qc.ry(theta[i], i)\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bd1c1a6-5a53-44c5-a431-5e12c9c6854f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from qiskit.primitives import Sampler\n",
    "#from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Options\n",
    "#service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
    "#backend = service.backend(\"ibm_quebec\")\n",
    "#options = Options(resilience_level = 1) # 1 = measurement error mitigation\n",
    "#sampler = Sampler(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5de6a2ac-d0d1-4f99-ba0d-0a9a69cf0824",
   "metadata": {},
   "outputs": [],
   "source": [
    "def quantum_nn(X, theta, simulator=True):\n",
    "    \n",
    "    qc, c = feature_map(X)\n",
    "    qc = variational_circuit(qc, theta)\n",
    "    qc.measure(0,c)\n",
    "    \n",
    "    shots = 1E4\n",
    "    backend = Aer.get_backend('qasm_simulator')\n",
    "    \n",
    "    if simulator == False:\n",
    "        \n",
    "        shots = 5000\n",
    "        #provider = IBMQ.load_account()\n",
    "        backend = service.backend(\"ibm_quebec\")\n",
    "        \n",
    "    job = qiskit.execute(qc, backend, shots=shots)\n",
    "    result = job.result()\n",
    "    counts = result.get_counts(qc)\n",
    "    \n",
    "    return(counts['1']/shots)\n",
    "\n",
    "    qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d08cc446-92e8-43b7-a9f8-784ca13b4216",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss (prediction, target):\n",
    "    return (prediction - target)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0e730d5-e3cc-4a66-b7c2-0ff2f0c2ebc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient(X, Y, theta):\n",
    "    \n",
    "    delta = 0.01\n",
    "    grad = []\n",
    "    for i in range(len(theta)):\n",
    "        \n",
    "        dtheta = copy.copy(theta)\n",
    "        dtheta[i] += delta\n",
    "        \n",
    "        pred1 = quantum_nn(X, dtheta)\n",
    "        pred2 = quantum_nn(X, theta)\n",
    "        \n",
    "        grad.append((loss(pred1, Y) - loss(pred2, Y)) / delta)\n",
    "        \n",
    "    return np.array(grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "367eab83-7c65-4075-baf7-1705eb8cef62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(X, Y, theta):\n",
    "    \n",
    "    counter = 0\n",
    "    for X_i, Y_i in zip(X, Y):\n",
    "        \n",
    "        prediction = quantum_nn(X_i, theta)\n",
    "        \n",
    "        if prediction < 0.5 and Y_i == 0:\n",
    "            counter += 1\n",
    "        elif prediction >= 0.5 and Y_i == 1:\n",
    "            counter += 1\n",
    "            \n",
    "    return counter/len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0607d333-ca8f-4b86-acc6-a4ba34c7fbcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch \t Loss \t Training Accuracy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:4: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(i, i+1)\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:5: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(N-1, 0)\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:4: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(i, i+1)\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:5: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(N-1, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \t 0.258 \t 0.800\n",
      "1 \t 0.234 \t 0.863\n",
      "2 \t 0.215 \t 0.838\n",
      "3 \t 0.208 \t 0.812\n",
      "4 \t 0.206 \t 0.812\n",
      "5 \t 0.211 \t 0.825\n",
      "6 \t 0.212 \t 0.812\n",
      "7 \t 0.205 \t 0.825\n",
      "8 \t 0.204 \t 0.838\n",
      "9 \t 0.216 \t 0.838\n",
      "10 \t 0.225 \t 0.800\n",
      "11 \t 0.223 \t 0.750\n",
      "12 \t 0.240 \t 0.750\n",
      "13 \t 0.259 \t 0.412\n",
      "14 \t 0.247 \t 0.787\n",
      "15 \t 0.217 \t 0.812\n",
      "16 \t 0.208 \t 0.800\n",
      "17 \t 0.207 \t 0.825\n",
      "18 \t 0.202 \t 0.812\n",
      "19 \t 0.202 \t 0.838\n",
      "20 \t 0.207 \t 0.787\n",
      "21 \t 0.217 \t 0.787\n",
      "22 \t 0.217 \t 0.850\n",
      "23 \t 0.234 \t 0.750\n",
      "24 \t 0.211 \t 0.800\n",
      "25 \t 0.209 \t 0.812\n",
      "26 \t 0.209 \t 0.838\n",
      "27 \t 0.204 \t 0.812\n",
      "28 \t 0.203 \t 0.787\n",
      "29 \t 0.203 \t 0.800\n",
      "30 \t 0.206 \t 0.825\n",
      "31 \t 0.205 \t 0.825\n",
      "32 \t 0.203 \t 0.800\n",
      "33 \t 0.203 \t 0.825\n",
      "34 \t 0.207 \t 0.838\n",
      "35 \t 0.207 \t 0.787\n",
      "36 \t 0.206 \t 0.800\n",
      "37 \t 0.204 \t 0.812\n",
      "38 \t 0.202 \t 0.825\n",
      "39 \t 0.203 \t 0.825\n",
      "40 \t 0.203 \t 0.812\n",
      "41 \t 0.204 \t 0.825\n",
      "42 \t 0.203 \t 0.812\n",
      "43 \t 0.202 \t 0.800\n",
      "44 \t 0.205 \t 0.787\n",
      "45 \t 0.204 \t 0.825\n",
      "46 \t 0.204 \t 0.787\n",
      "47 \t 0.204 \t 0.800\n",
      "48 \t 0.203 \t 0.800\n",
      "49 \t 0.203 \t 0.825\n"
     ]
    }
   ],
   "source": [
    "eta = 0.075\n",
    "loss_list = []\n",
    "theta = np.ones(N)\n",
    "\n",
    "print('Epoch \\t Loss \\t Training Accuracy')\n",
    "\n",
    "for i in range(50):\n",
    "    \n",
    "    loss_tmp = []\n",
    "    for X_i, Y_i in zip(X_train, Y_train):\n",
    "\n",
    "        prediction = quantum_nn(X_i, theta)\n",
    "        loss_tmp.append(loss(prediction, Y_i))\n",
    "        theta = theta - eta * gradient(X_i, Y_i, theta)\n",
    "        \n",
    "    loss_list.append(np.mean(loss_tmp))\n",
    "    acc_train = accuracy(X_train, Y_train, theta)\n",
    "    \n",
    "    print(f'{i} \\t {loss_list[-1]:.3f} \\t {acc_train:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7143545-fc9e-4edd-8894-9d9f6758db1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_list)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2c662fb-07b0-406e-9a3d-7318bc28473f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:4: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(i, i+1)\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_20924\\2434704148.py:5: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.cnot()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use QuantumCircuit.cx as direct replacement.\n",
      "  qc.cnot(N-1, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QNN classification train score: 0.825\n",
      "QNN classification test score: 0.8\n"
     ]
    }
   ],
   "source": [
    "acc_test = accuracy(X_test, Y_test, theta)\n",
    "print(f\"QNN classification train score: {acc_train}\")\n",
    "print(f\"QNN classification test score: {acc_test}\")"
   ]
  }
 ],
 "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}