Task 1 Final-General_Circuit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "250b7948",
   "metadata": {},
   "source": [
    "## Task 1\n",
    "\n",
    "### This program builds a quantum circuit that accepts any vector of size $4$ and encodes the data using qRAM and finds the address of data which doesn't have similar digits consecutively. \n",
    "\n",
    "#### Since finding $2$ marked solutions in the array of $4$ is impossible using grover's algorithm. Here we used a trich which uses $3$ qbits for address so storing $2^{3}=8$ data points (While measuring we just measure required two). Remaining 4 dummy data points are initiated as $0$ (Doesn't make any difference) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c495377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing standard Qiskit libraries\n",
    "from qiskit import IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "\n",
    "# Loading your IBM Quantum account\n",
    "IBMQ.load_account()\n",
    "IBMQ.providers() # see a list of providers you have access to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1fc80af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit,  transpile\n",
    "from qiskit import IBMQ, Aer, execute\n",
    "from qiskit.visualization import plot_bloch_multivector\n",
    "# import basic plot tools\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc061ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0001', '0101', '0111', '1010', '0000', '0000', '0000', '0000']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_st = [1,5,7,10]  #change input here\n",
    "\n",
    "#passing in to binary representation\n",
    "bin_pass = []\n",
    "for num in input_st:\n",
    "    bin_pass.append('{0:04b}'.format(num))\n",
    "for i in range(4):\n",
    "    bin_pass.append('0000')             #appending the rest of zeroes (dummy data)\n",
    "\n",
    "bin_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "979e591d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Oracle which checks for th\n",
    "def oracle(qc,data,comput):\n",
    "    #xor of first two digits\n",
    "    qc.cx(data[0], comput[0])\n",
    "    qc.cx(data[1], comput[0])\n",
    "\n",
    "    #xor of digit 1 and 2\n",
    "    qc.cx(data[1], comput[1])\n",
    "    qc.cx(data[2], comput[1])\n",
    "\n",
    "    #xor of digit 2 and 3\n",
    "    qc.cx(data[2], comput[2])\n",
    "    qc.cx(data[3], comput[2])\n",
    "    qc.barrier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0df52101",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffuser(nqubits):\n",
    "    qc = QuantumCircuit(nqubits)\n",
    "    # Apply transformation |s> -> |00..0> (H-gates)\n",
    "    for qubit in range(nqubits):\n",
    "        qc.h(qubit)\n",
    "    # Apply transformation |00..0> -> |11..1> (X-gates)\n",
    "    for qubit in range(nqubits):\n",
    "        qc.x(qubit)\n",
    "    # Do multi-controlled-Z gate\n",
    "    qc.h(nqubits-1)\n",
    "    qc.mct(list(range(nqubits-1)), nqubits-1)  # multi-controlled-toffoli\n",
    "    qc.h(nqubits-1)\n",
    "    # Apply transformation |11..1> -> |00..0>\n",
    "    for qubit in range(nqubits):\n",
    "        qc.x(qubit)\n",
    "    # Apply transformation |00..0> -> |s>\n",
    "    for qubit in range(nqubits):\n",
    "        qc.h(qubit)\n",
    "    # We will return the diffuser as a gate\n",
    "    U_s = qc.to_gate()\n",
    "    U_s.name = \"$U_s$\"\n",
    "    return U_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75bc9076",
   "metadata": {},
   "outputs": [],
   "source": [
    "address = QuantumRegister(3)\n",
    "data = QuantumRegister(4)\n",
    "comput = QuantumRegister(3)\n",
    "flag = QuantumRegister(1)\n",
    "# c = ClassicalRegister(4) #Only measuring the qRAM data\n",
    "c = ClassicalRegister(2) #Only measuring the qRAM address\n",
    "# c = ClassicalRegister(3) #Only measuring the comput and qRAM address\n",
    "anicilla_qubit = QuantumRegister(2)\n",
    "qRAM_ancilla = QuantumRegister(1)\n",
    "qc = QuantumCircuit(address,data,qRAM_ancilla,comput,flag,anicilla_qubit,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "981ee7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x14550ec0850>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.x(flag[0]) #Flag for qram check\n",
    "qc.h(flag[0])\n",
    "# address preparation\n",
    "qc.h([address[0],address[1],address[2]])\n",
    "qc.barrier()\n",
    "\n",
    "\n",
    "for l in range(1):\n",
    "    #qRAM starts here\n",
    "    for k in range(2):\n",
    "        for j in range(2):\n",
    "            for i in range(2):\n",
    "                qc.barrier()\n",
    "                flagi = flagj = flagk= 0\n",
    "                if i==0:\n",
    "                    qc.x([address[0]])\n",
    "                    flagi=1\n",
    "                if j==0:\n",
    "                    qc.x([address[1]])\n",
    "                    flagj=1                    \n",
    "                if k==0:\n",
    "                    qc.x([address[2]])\n",
    "                    flagk=1\n",
    "                for m in range(4):\n",
    "                    if bin_pass[4*k+2*j+1*i][m]=='1':\n",
    "                        qc.mct(address,data[m],qRAM_ancilla,mode='basic')\n",
    "                if flagi==1:\n",
    "                    qc.x([address[0]])\n",
    "                if flagj==1:\n",
    "                    qc.x([address[1]])                \n",
    "                if flagk==1:\n",
    "                    qc.x([address[2]])   \n",
    "#     qc.measure(data[:], c) #to check working of qRAM\n",
    "    #initiating the data = zero in the rest memory\n",
    "    qc.barrier()\n",
    "    oracle(qc,data,comput)\n",
    "    qc.barrier()\n",
    "    # mct to see if we are getting 111\n",
    "    qc.mct(comput,flag[0],anicilla_qubit,mode='basic')\n",
    "    qc.barrier()\n",
    "    oracle(qc,data,comput)\n",
    "    qc.barrier()\n",
    "#     #qRAM again for uncomputation \n",
    "    #qRAM starts here\n",
    "    for k in range(2):\n",
    "        for j in range(2):\n",
    "            for i in range(2):\n",
    "                qc.barrier()\n",
    "                flagi = flagj = flagk= 0\n",
    "                if i==0:\n",
    "                    qc.x([address[0]])\n",
    "                    flagi=1\n",
    "                if j==0:\n",
    "                    qc.x([address[1]])\n",
    "                    flagj=1                    \n",
    "                if k==0:\n",
    "                    qc.x([address[2]])\n",
    "                    flagk=1\n",
    "                for m in range(4):\n",
    "                    if bin_pass[4*k+2*j+1*i][m]=='1':\n",
    "                        qc.mct(address,data[m],qRAM_ancilla,mode='basic')\n",
    "                if flagi==1:\n",
    "                    qc.x([address[0]])\n",
    "                if flagj==1:\n",
    "                    qc.x([address[1]])                \n",
    "                if flagk==1:\n",
    "                    qc.x([address[2]]) \n",
    "    qc.barrier()\n",
    "    qc.append(diffuser(3),[0,1,2])\n",
    "    \n",
    "qc.barrier()\n",
    "# qc = qc.reverse_bits()\n",
    "qc.measure(address[0:2], c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dd93f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1604.87x3816.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd91d349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth: 160\n",
      "Gate counts: OrderedDict([('cx', 66), ('u2', 41), ('x', 32), ('barrier', 25), ('tdg', 18), ('t', 18), ('ccx', 18), ('h', 3), ('u1', 2), ('measure', 2)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sumit\\miniconda3\\envs\\ibmqiskit\\lib\\site-packages\\qiskit\\execute_function.py:399: DeprecationWarning: Using `backend_options` kwarg has been deprecated as of qiskit-aer 0.7.0 and will be removed no earlier than 3 months from that release date. Runtime backend options should now be added directly using kwargs for each option.\n",
      "  job = backend.run(experiments, **run_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend = Aer.get_backend('qasm_simulator')\n",
    "for kk in range(2):\n",
    "    new_cirq = transpile(qc, backend, optimization_level=kk)\n",
    "#     print('Optimization Level {}'.format(kk))\n",
    "#     print('Depth:', new_cirq.depth())\n",
    "#     print('Gate counts:', new_cirq.count_ops())\n",
    "#     print()\n",
    "print('Depth:', new_cirq.depth())\n",
    "print('Gate counts:', new_cirq.count_ops())\n",
    "job = execute(new_cirq, backend=backend, shots=8192, seed_simulator=12345, backend_options={\"fusion_enable\":True})\n",
    "#job = execute(qc, backend=backend, shots=8192)\n",
    "result = job.result()\n",
    "count =result.get_counts()\n",
    "plot_histogram(result.get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "869511f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'01': 4139, '11': 4053}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e52f7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was the given input\n",
      " [1, 5, 7, 10]\n",
      "\n",
      "This is binary conversion of the solutions\t:0101 \t 1010\n"
     ]
    }
   ],
   "source": [
    "print('This was the given input\\n',input_st)\n",
    "print('\\nThis is binary conversion of the solutions\\t:''{0:04b}'.format(5),'\\t','{0:04b}'.format(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a22f8cee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ibmqiskit",
   "language": "python",
   "name": "ibmqiskit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}