Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c7efa27-03b4-4ffe-a56e-faaff9ee81c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ee8339-fc82-4144-b12a-d85e400af46c",
   "metadata": {},
   "source": [
    "# Basis Encoding\n",
    "This example illustrates a couple of disadvantages of basis encoding. While it is simple to understand, the state vectors can become quite sparse, and schemes to implement it are usually not efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cac8c10-c31b-4123-84f5-d1f0462234a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.997x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "desired_state = [\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    1 / math.sqrt(2),\n",
    "    0,\n",
    "    1 / math.sqrt(2)]\n",
    "\n",
    "qc = QuantumCircuit(3)\n",
    "qc.initialize(desired_state, [0,1,2])\n",
    "\n",
    "qc.decompose().decompose().decompose().decompose().decompose().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cfa0b1f-5539-475e-827b-b93b875d0306",
   "metadata": {},
   "source": [
    "# Amplitude Encoding\n",
    "The advantage of amplitude encoding is that it only requires log_2(NM) qubits to encode. However, subsequent algorithms must operate on the amplitudes of a quantum state, and methods to prepare and measure the quantum states tend not to be efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8d0e015-9c1f-4173-892d-bbd356e70b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 688.452x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desired_state = [\n",
    "    1 / math.sqrt(15.25) * 1.5,\n",
    "    0,\n",
    "    1 / math.sqrt(15.25) * -2,\n",
    "    1 / math.sqrt(15.25) * 3]\n",
    "\n",
    "qc = QuantumCircuit(2)\n",
    "qc.initialize(desired_state, [0,1])\n",
    "\n",
    "qc.decompose().decompose().decompose().decompose().decompose().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76d31f6-c371-4e3c-947e-ad7e2d0de984",
   "metadata": {},
   "source": [
    "# Angle Encoding\n",
    "This is different from the previous two encoding methods, as it only encodes one datapoint at a time, rather than a whole dataset. It does however, only use  qubits and a constant depth quantum circuit, making it amenable to current quantum hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05b96f9b-52fe-49d0-b670-921020c9e9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 146.797x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(3)\n",
    "\n",
    "qc.ry(0, 0)\n",
    "qc.ry(math.pi/4, 1)\n",
    "qc.ry(math.pi/2, 2)\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c304c3-bcc4-4349-a323-9c65c41f3ac9",
   "metadata": {},
   "source": [
    "# Constant Depth Arbitrary Encoding\n",
    "\n",
    "Like angle encoding, it only encodes one datapoint at a time, rather than a whole dataset. It also uses a constant depth quantum circuit and  qubits, meaning it can be run on current quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b216cc90-a701-4a17-a82d-1cb8637dab02",
   "metadata": {},
   "source": [
    "## ZZFeatureMap\n",
    "The Qiskit ZZFeatureMap circuit with 3 qubits, only encodes a datapoint of 3 features, despite having 6 parameterized gates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2ea8959-bfcb-4bbd-834e-6698b7b2573d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170.2x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "\n",
    "circuit = ZZFeatureMap(3, reps=1, insert_barriers=True)\n",
    "circuit.decompose().draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8fbc042-7746-479a-9f91-0b4cdc1a5473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.597x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [0.1, 0.2, 0.3]\n",
    "\n",
    "encode = circuit.bind_parameters(x)\n",
    "encode.decompose().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7064e75-54fa-438b-8584-e2a6f625e09c",
   "metadata": {},
   "source": [
    "## EfficientSU2\n",
    "Qiskit EfficientSU2 circuit to encode 12 features, would only use 3 qubits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a427fc3-3a2f-4f1d-ba24-2ce3cf47cd12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 628.397x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import EfficientSU2\n",
    "\n",
    "circuit = EfficientSU2(num_qubits=3, reps=1, insert_barriers=True)\n",
    "circuit.decompose().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58942e2a-15d7-435c-bbad-d7b2fb0ec4c2",
   "metadata": {},
   "source": [
    "The datapoint x with 12 features, using each of the parameterized gates to encode a different feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f70c978-5601-4567-8ef3-7b0146f9d961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 628.397x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]\n",
    "\n",
    "encode = circuit.bind_parameters(x)\n",
    "encode.decompose().draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84b073b3-4d4a-46e3-9c3e-19e3d415231e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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