toDec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71073f8-6acf-413e-9ef5-eb61c456bf3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2022-02-24 02:43:09,167: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, execute, transpile, Aer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "import math\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()\n",
    "print('Libraries imported successfully!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "6c40dd8d-8e51-4079-96d7-287c4a794861",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def initQC(maxVal_ones, maxVal_tenths, num_of_places):\n",
    "    ones_bits = int(math.log(maxVal_ones+1, 2))\n",
    "    tenths_bits = int(math.log(maxVal_tenths+1, 2))\n",
    "    total_bits = ones_bits + tenths_bits*num_of_places\n",
    "    \n",
    "    qc = QuantumCircuit(total_bits, total_bits)\n",
    "    return qc, int(total_bits)\n",
    "\n",
    "def demoManipulate(qc, num_of_bits):\n",
    "    for qubit_idx in range(num_of_bits):\n",
    "        if qubit_idx%4 == 1:\n",
    "            qc.x(qubit_idx)\n",
    "    #qc.x([i for i in range(11)])\n",
    "    qc.measure_all()\n",
    "    return qc\n",
    "\n",
    "def constructDec():\n",
    "    # 3, 7, 3\n",
    "    qc, numOfBits = initQC(3, 7, 3)\n",
    "    demoManipulate(qc, numOfBits)\n",
    "    \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "e098024d-933b-44ed-a0b5-62a275caf6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 899.479x806.68 with 1 Axes>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myQC = constructDec()\n",
    "myQC.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "a3a232db-fb99-4ba1-8d49-2374f59752fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01000100010 00000000000': 1024}\n"
     ]
    }
   ],
   "source": [
    "backend = Aer.get_backend('qasm_simulator') # Tell it which simulator you want to use\n",
    "job = execute(myQC,backend, shots = 1024) # Put in the name of your quantum circuit where it says qc, and the number of shots you want to use\n",
    "result = job.result()\n",
    "\n",
    "counts = result.get_counts()\n",
    "print(counts)\n",
    "#plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "51aa601e-4156-46b0-aae6-2e9c1cfa5f62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0 1 10\n"
     ]
    }
   ],
   "source": [
    "for key in counts.keys():\n",
    "    thousandths_place_digit = int(key[8:11][::-1])\n",
    "    hundredths_place_digit = int(key[5:8][::-1])\n",
    "    tenths_place_digit = int(key[2:5][::-1])\n",
    "    ones_place_digit = int(key[0:2][::-1])\n",
    "print(ones_place_digit, tenths_place_digit, hundredths_place_digit, thousandths_place_digit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
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    "def binaryToDecimal(binary):\n",
    "    binary1 = binary\n",
    "    decimal, i, n = 0, 0, 0\n",
    "    while(binary != 0):\n",
    "        dec = binary % 10\n",
    "        decimal = decimal + dec * pow(2, i)\n",
    "        binary = binary//10\n",
    "        i += 1\n",
    "    return decimal  "
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    "binHundreths = str(binaryToDecimal(hundredths_place_digit))\n",
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