Distance Based Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum Distance Based Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Follows the implementation of the Quantum Distance Based Classifier by Schuld, Fingerhuth and Petruccione\n",
    "#### For more details: https://arxiv.org/abs/1703.10793 [1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To prevent inconsistencies, I have to notice you that in [1], the circuit starts by superposing both |a> and |m>.  In my circuit, I decided to postpone the application of H on |m> for an illustrative purpose. However, this difference doesn't affect in any way the computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer, Aer\n",
    "from qiskit.extensions import Initialize, UnitaryGate\n",
    "from qiskit.visualization import plot_histogram, plot_bloch_multivector, plot_state_city\n",
    "#from qiskit_textbook.tools import random_state, array_to_latex\n",
    "from utility import qdrawer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QDistanceBasedClassifier:\n",
    "    \n",
    "####################################################################\n",
    "    def get_circuit(self):\n",
    "        return self.qc\n",
    "        \n",
    "####################################################################\n",
    "        \n",
    "    def _step_A(self, a):\n",
    "        '''Uniform superpos of ancilla and index qubit'''\n",
    "        self.qc.h(a)\n",
    "        self.qc.barrier()\n",
    "\n",
    "####################################################################\n",
    "\n",
    "    def _step_B(self, alpha, a, b, c):\n",
    "        '''Loading test vector  '''\n",
    "        self.qc.mcry(alpha, a, c[0], None) \n",
    "        self.qc.barrier()\n",
    "        self.qc.x(a)\n",
    "        self.qc.barrier()\n",
    "        self.qc.h(b)\n",
    "        self.qc.barrier()\n",
    "\n",
    "        \n",
    "####################################################################\n",
    "\n",
    "    def _step_C(self, a, b, c):\n",
    "        '''Loading first training vector'''\n",
    "        self.qc.ccx(a,b,c)\n",
    "        self.qc.x(b)\n",
    "        self.qc.barrier()\n",
    "\n",
    "####################################################################\n",
    "        \n",
    "    def _step_D(self, alpha, a, b, c, d):\n",
    "        '''Load second training vector'''\n",
    "        self.qc.mcry(alpha, a[0:]+b[0:], c[0], None) \n",
    "        self.qc.barrier()\n",
    "        self.qc.cx(b, d)\n",
    "        self.qc.barrier()\n",
    "        \n",
    "####################################################################\n",
    "\n",
    "    def _step_F(self, a):\n",
    "        '''Compute distance'''\n",
    "        self.qc.h(a)\n",
    "        self.qc.barrier()\n",
    "        \n",
    "####################################################################\n",
    "\n",
    "    def step_measure(self):\n",
    "        '''Measurements'''\n",
    "        self.qc.measure(self.aqr, self.c_areg)\n",
    "        self.qc.measure(self.cqr, self.c_creg)\n",
    "        \n",
    "####################################################################\n",
    "\n",
    "    def _init_qc(self):\n",
    "        aqr = QuantumRegister(1, name=\"ancilla \")\n",
    "        mqr = QuantumRegister(1, name=\"index qregister \") #Just one since we have only two training inputs\n",
    "        iqr = QuantumRegister(1, name=\"data register \")\n",
    "        cqr = QuantumRegister(1, name=\"classes \")\n",
    "\n",
    "        c_areg = ClassicalRegister(1, name=\"classical ancilla \")\n",
    "        c_creg = ClassicalRegister(1, name=\"classical classes \")\n",
    "        \n",
    "        qc = QuantumCircuit(aqr, mqr, iqr, cqr, c_areg, c_creg)\n",
    "        return qc, aqr, mqr, iqr, cqr, c_areg, c_creg\n",
    "    \n",
    "####################################################################\n",
    "\n",
    "    def __init__(self, alpha_tr_1, alpha_test):\n",
    "        self.qc, self.aqr, self.mqr, self.iqr, self.cqr, self.c_areg, self.c_creg = self._init_qc()\n",
    "        \n",
    "        #Uniform superpos of ancilla and index qubits\n",
    "        self._step_A(self.aqr)\n",
    "\n",
    "        #Loading test vector\n",
    "        self._step_B(alpha_test, self.aqr, self.mqr, self.iqr)\n",
    "\n",
    "        #Loading first training vector\n",
    "        self._step_C(self.aqr, self.mqr, self.iqr)\n",
    "\n",
    "        #Load second training vector\n",
    "        self._step_D(alpha_tr_1, self.aqr, self.mqr, self.iqr, self.cqr)\n",
    "\n",
    "        #compute distance\n",
    "        self._step_F(self.aqr)\n",
    "        \n",
    "        self.step_measure()     \n",
    "\n",
    "####################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Vector's alpha: 4.30373230357443\n",
      "Training_1 Vector's alpha: 1.3241904926325558\n"
     ]
    }
   ],
   "source": [
    "#Follows the paper's inputs\n",
    "\n",
    "test_1 = [-0.549, 0.836]   \n",
    "tr_0 = [0,1] \n",
    "tr_1 = [0.789, 0.615]\n",
    "\n",
    "\n",
    "#Computing the rotation angle\n",
    "alpha_test = np.arctan2(test_1[1], test_1[0])*2\n",
    "alpha_tr_1 = np.arctan2(tr_1[1], tr_1[0])*2\n",
    "print(\"Test Vector's alpha:\", alpha_test)\n",
    "print(\"Training_1 Vector's alpha:\", alpha_tr_1)\n",
    "#using arcatan2 --> Obtain the correct quadrans. Otherwise, with arctan(y/x) you cannot get the correct quadrant\n",
    "#ex: -1.52 can be obtained from (-y/x) and (y/-x)\n",
    "\n",
    "\n",
    "q_distance_based_classifier = QDistanceBasedClassifier(alpha_tr_1, alpha_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1742.74x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_distance_based_circuit = q_distance_based_classifier.get_circuit()\n",
    "q_distance_based_circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Dataset (Unitary circle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qdrawer.draw_qubit()\n",
    "qdrawer.draw_quantum_state(test_1[0],test_1[1],\"test\", color='orange')\n",
    "qdrawer.draw_quantum_state(tr_1[0],tr_1[1],\"training_1\", color='orange')\n",
    "qdrawer.draw_quantum_state(0, 1,\"training_0\", color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "job = execute(q_distance_based_circuit, simulator, shots=4096)\n",
    "result = job.result()\n",
    "counts = result.get_counts(q_distance_based_circuit)\n",
    "counts = {k: counts[k] for k in counts.keys() & {'0 0', '1 0'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts, title=\"Classes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On x axis, the highest bit indicates the output class. So:\n",
    "#### 00 --> test vector belongs to class 0\n",
    "#### 10 --> test vector belongs to class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}