NB7-Twobit_Addition-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Works Fine for a<=8 and b<=7"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "A half adder takes two inputs and produces one output and a carry-out bit.\n",
    "A full adder takes three inputs ( two bits + one carry-in bit) and produces one output and a carry-out bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, execute, Aer\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0b111 111\n"
     ]
    }
   ],
   "source": [
    "#Assign First number\n",
    "a=7\n",
    "bin_a=bin(a)\n",
    "print (bin_a,bin_a[2:])\n",
    "a_bin=bin_a[2:]\n",
    "#a_bin=a_bin[::-1] #reversing the list\n",
    "#a_bin[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0b111 111\n",
      "111 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'111'"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assign second number\n",
    "b=7\n",
    "bin_b=bin(b)\n",
    "print (bin_b,bin_b[2:])\n",
    "b_bin=bin_b[2:]\n",
    "print (b_bin,b_bin[0])\n",
    "#b_bin=b_bin[::-1] # Reversing the list \n",
    "b_bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\quad11$\\\n",
    "$\\;+11$\\\n",
    "$=110$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "a: 111 length: 3\n",
      "b: 111 length 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making sure they have same number of bits for addition\n",
    "diff=abs(len(a_bin)-len(b_bin))\n",
    "print (diff)\n",
    "if len(a_bin)<len(b_bin):\n",
    "    a_bin=a_bin.zfill(diff+len(a_bin))\n",
    "else:\n",
    "    b_bin=b_bin.zfill(diff+len(b_bin))\n",
    "print ('a:',a_bin,'length:',len(a_bin))\n",
    "print ('b:',b_bin,'length',len(b_bin))\n",
    "b_bin[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0b1110 1110\n"
     ]
    }
   ],
   "source": [
    "c=a+b\n",
    "bin_c=bin(c)\n",
    "print (bin_c,bin_c[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of inputs: 10 \n",
      "No of outputs: 4\n"
     ]
    }
   ],
   "source": [
    "# Adding a and b using quantum circuit to get c. Checking size of QR and CR!\n",
    "inputs=len(a_bin)+len(b_bin)+len(bin_c[2:])\n",
    "outputs=len(bin_c[2:])\n",
    "print (\"No of inputs:\",inputs,\"\\nNo of outputs:\",outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1240.12x686.28 with 1 Axes>"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc=QuantumCircuit(inputs,outputs)\n",
    "qc.barrier()\n",
    "#Adding x-gates\n",
    "for i in range(len(a_bin)):\n",
    "    if a_bin[i]=='1':\n",
    "        qc.x(i)\n",
    "       \n",
    "    if b_bin[i]=='1':\n",
    "        qc.x(len(a_bin)+i)\n",
    "        \n",
    "#Adding cx-gates\n",
    "for i in range(len(a_bin)):\n",
    "    if a_bin[i]=='1':\n",
    "        #qc.x(i)\n",
    "        qc.cx(i,3*len(a_bin)-i-1)\n",
    "    if b_bin[i]=='1':\n",
    "        qc.cx(len(a_bin)+i, 3*len(a_bin)-i-1)\n",
    "qc.barrier()\n",
    "       \n",
    "#Adding ccx-gates\n",
    "for i in range(len(a_bin)):\n",
    "    if a_bin[i]=='1' and b_bin[i]=='1':\n",
    "        #print(i)\n",
    "        qc.ccx(i,len(a_bin)+i,inputs-i-1)\n",
    "qc.barrier()\n",
    "\n",
    "for i in range(len(a_bin)):\n",
    "    if i>0:\n",
    "        if a_bin[i]=='1' and b_bin[i]=='1':\n",
    "            #print(a_bin[i-1],b_bin[i-1])\n",
    "            if a_bin[i-1]=='1' or b_bin[i-1]=='1':\n",
    "                #print(a_bin[i-1],b_bin[i-1])\n",
    "                if a_bin[i-1] != b_bin[i-1]:\n",
    "                    #print(a_bin[i-1],b_bin[i-1])\n",
    "                    qc.ccx(i,len(a_bin)+i,inputs-i)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "\n",
    "for i in range(outputs):\n",
    "    #print (len(a_bin)+len(b_bin)+i,i)\n",
    "    qc.measure(len(a_bin)+len(b_bin)+i,i)\n",
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = execute(qc,Aer.get_backend('qasm_simulator')).result().get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= 7 \n",
      "b= 7 \n",
      "sum(c)= 14 \n",
      "Equivalent binary of c: 1110\n"
     ]
    }
   ],
   "source": [
    "print ('a=',a,'\\nb=',b,'\\nsum(c)=',c, '\\nEquivalent binary of c:',bin(c)[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}