{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support material\n",
    "#### Direct generation of the quantum circuit for an iterative quantamorphism (qfor-loop) parametric on the number of qubits of the control and comparison with that generated by a unitary in Quippe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Useful additional packages \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "from qiskit.tools.visualization import circuit_drawer\n",
    "from qiskit.quantum_info import state_fidelity\n",
    "from qiskit import BasicAer\n",
    "\n",
    "backend = BasicAer.get_backend('unitary_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Union, NoReturn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $qfor f (n,b)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 3.141592653589793)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def what_f(f):\n",
    "    t = p = l = 0\n",
    "    if f == 'X':\n",
    "        t = pi\n",
    "        p = 0\n",
    "        l = pi\n",
    "    elif f == 'Y':\n",
    "        t = pi\n",
    "        p = pi/2\n",
    "        l = pi/2\n",
    "    elif f == 'Z':\n",
    "        t = 0\n",
    "        p = 0\n",
    "        l = pi\n",
    "    elif f == 'H':\n",
    "        t = pi/2\n",
    "        p = 0\n",
    "        l = pi\n",
    "    elif f == 'S':\n",
    "        t = 0\n",
    "        p = 0\n",
    "        l = pi/2\n",
    "    elif f == 'Sdg':\n",
    "        t = 0\n",
    "        p = 0\n",
    "        l = -pi/2\n",
    "    elif f == 'T':\n",
    "        t = 0\n",
    "        p = 0\n",
    "        l = pi/4\n",
    "    elif f == 'Tdg':\n",
    "        t = 0\n",
    "        p = 0\n",
    "        l = -pi/4\n",
    "    # (theta, phi, lambda)\n",
    "    return (t, p, l)   \n",
    "\n",
    "# test\n",
    "what_f('Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assert_never(x: NoReturn) -> NoReturn:\n",
    "    raise AssertionError(\"Unhandled type: {}\".format(type(x).__name__))\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class Q:\n",
    "    value: int\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class T:\n",
    "    pass\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class F:\n",
    "    pass\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class And:\n",
    "    left: 'BExp'\n",
    "    right: 'BExp'\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class Or:\n",
    "    left: 'BExp'\n",
    "    right: 'BExp'\n",
    "\n",
    "BExp = Union[Q, T, F, And, Or]\n",
    "\n",
    "def showBExp(r: BExp) -> str:\n",
    "    if isinstance(r, T):\n",
    "        return \"1\"\n",
    "    elif isinstance(r, F):\n",
    "        return \"0\"\n",
    "    elif isinstance(r, Q):\n",
    "        return (\"q\" + str(r.value))\n",
    "    elif isinstance(r, And):\n",
    "        return (showBExp(r.left) + \"*\" + showBExp(r.right))\n",
    "    elif isinstance(r, Or):\n",
    "        return (\"(\" + showBExp(r.left) + \"+\" + showBExp(r.right) + \")\")\n",
    "    else:\n",
    "        assert_never(r)\n",
    "\n",
    "def andBExp(a: BExp, b : BExp) -> BExp:\n",
    "    if isinstance(b, T):\n",
    "        return a\n",
    "    elif isinstance(a, T):\n",
    "        return b\n",
    "    else:\n",
    "        return And(a, b)\n",
    "\n",
    "def orBExp(a: BExp, b : BExp) -> BExp:\n",
    "    if isinstance(b, T):\n",
    "        return T()\n",
    "    elif isinstance(a, T):\n",
    "        return T()\n",
    "    else:\n",
    "        return Or(a, b)\n",
    "\n",
    "def f(n):\n",
    "    if n == 0:\n",
    "        return [Q(0), T()]\n",
    "    else:\n",
    "        x = f(n - 1)\n",
    "        y = f(n - 1) # useless, x is the same value\n",
    "        return ([andBExp(Q(n), e) for e in x] + [orBExp(Q(n), e) for e in y])\n",
    "\n",
    "# assumes n >= 0\n",
    "def main0(n : int):\n",
    "    pred = n  - 1\n",
    "\n",
    "    l = f(pred)\n",
    "    l.reverse()\n",
    "\n",
    "    zero_to_len = list(range(0, len(l)))\n",
    "    zipped = list(zip(zero_to_len, l))\n",
    "    zipped.reverse()\n",
    "\n",
    "    toPrint = \"\"\n",
    "    l =[]\n",
    "    for (n, s) in zipped:\n",
    "        #toPrint += (\"\\n(>= \" + str(n) + \") = \" + showBExp(s))\n",
    "        l.append(showBExp(s))\n",
    "    return l\n",
    "    #print(toPrint + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '0q+1q+2q', '1q+2q', '0q*1q+2q', '2q', '0q+1q*2q', '1q*2q', '0q*1q*2q']\n"
     ]
    }
   ],
   "source": [
    "def what_n(n):\n",
    "    l = main0(n)\n",
    "    cl=[]\n",
    "    for x in l:\n",
    "        x = \"\".join([char for char in x if char != \")\" and char != \"(\"])\n",
    "        cl.append(x) \n",
    "    rl =[x[::-1] for x in cl]\n",
    "    return rl[::-1]\n",
    "\n",
    "#test\n",
    "print(what_n(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qfor_init(f,n):\n",
    "    (t,p,l) = what_f(f)\n",
    "    list_controls = what_n(n)\n",
    "    print('OPENQASM 2.0;\\ninclude \"qelib1.inc\";')\n",
    "\n",
    "    if n <= 1:\n",
    "        # the number of qubits\n",
    "        qn=n\n",
    "        print('qreg q[%d];'%(n+1))\n",
    "        print('\\n')\n",
    "    else: \n",
    "        # the number of qubits\n",
    "        qn = n + n\n",
    "        print('qreg q[%d];'%qn)\n",
    "    qfor(f,n,list_controls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writting pseudo qasm\n",
    "def qfor(f, qn, lnn, d=0):\n",
    "    # f   - function <str>\n",
    "    # qn  - corresponds to the number of controls <int>\n",
    "    # lnn - type of controls <lst>\n",
    "    # d   - decomposition level <int>\n",
    "\n",
    "    # defining the funtion f \n",
    "    (theta,phi,lambdaa) = what_f(f)\n",
    "    # target\n",
    "    tg = 0\n",
    "    if lnn == []:\n",
    "        return\n",
    "    # g - gate we are implementing\n",
    "    g=lnn.pop(0)\n",
    "    # lg - instruction size\n",
    "    lg = len(g)        \n",
    "    try:\n",
    "        aj = [d*3+2 for d in range(qn-1)]\n",
    "        jj = aj + aj[::-1]\n",
    "        j = jj[d]\n",
    "        if g[j]=='*':\n",
    "            # no need for decompostion\n",
    "            if lg == 5 and d==0:\n",
    "                print('ccx q[%d], q[%d], q[%d];'%(qn,(qn-1),(qn+1)))\n",
    "                print('cu3 (%0.3f, %0.3f, %0.3f) q[%d], q[%d];'%(theta,phi,lambdaa,(qn+1),tg))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%(qn,(qn-1),(qn+1)))\n",
    "                d=0\n",
    "            # first gate in the decomposition \n",
    "            elif d==0:\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((tg+1),(tg+2),(qn+1)))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            #last instruction\n",
    "            elif d==qn+(qn-3):\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((tg+1),(tg+2),(qn+1)))\n",
    "                d=0\n",
    "            #reverse\n",
    "            elif d>qn-2:\n",
    "                #gates in decomposition\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((qn+(qn-1)-d),(qn*2-d+(qn-3)),(qn*2-d+(qn-2))))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            #other gates in decomposition\n",
    "            else:\n",
    "                #gates in decomposition\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((d+2),(qn+d),(qn+d+1)))\n",
    "                #last gate in decomposition\n",
    "                if d==qn-2:\n",
    "                    print('cu3(%0.3f, %0.3f, %0.3f) q[%d], q[%d];'%(theta,phi,lambdaa,(qn*2-1),tg))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            qfor(f,qn,lnn,d)\n",
    "        elif g[j]=='+':\n",
    "            # no need for decomposition\n",
    "            if lg == 5 and d==0:\n",
    "                print('x q[%d];'%(qn-1))\n",
    "                print('x q[%d];'%qn)\n",
    "                print('ccx q[%d], q[%d], q[%d];'%(qn,(qn-1),(qn+1)))\n",
    "                print('x q[%d];'%(qn+1))\n",
    "                print('cu3 (%0.3f, %0.3f, %0.3f) q[%d], q[%d];'%(theta,phi,lambdaa,(qn+1),tg))\n",
    "                print('x q[%d];'%(qn+1))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%(qn,(qn-1),(qn+1)))\n",
    "                print('x q[%d];'%(qn-1))\n",
    "                print('x q[%d];'%qn)\n",
    "                d=0\n",
    "            # first gate in the decomposition \n",
    "            elif d==0:\n",
    "                print('x q[%d];'%(tg+1))\n",
    "                print('x q[%d];'%(tg+2))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((tg+1),(tg+2),(qn+1)))\n",
    "                print('x q[%d];'%(qn+1))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            #last instruction\n",
    "            elif d==qn+(qn-3):\n",
    "                print('x q[%d];'%(qn+1))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((tg+1),(tg+2),(qn+1)))\n",
    "                print('x q[%d];'%(tg+1))\n",
    "                print('x q[%d];'%(tg+2))\n",
    "                d=0\n",
    "            #reverse\n",
    "            elif d>qn-2:\n",
    "                #gates in decomposition\n",
    "                print('x q[%d];'%(qn*2+1-d))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((qn+(qn-1)-d),(qn*2-d+(qn-3)),(qn*2-d+(qn-2))))\n",
    "                print('x q[%d];'%(qn+2-d))\n",
    "                print('x q[%d];'%(qn*2-d))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            #other gates in decomposition\n",
    "            else:\n",
    "                #gates in decomposition\n",
    "                print('x q[%d];'%(d+2))\n",
    "                print('x q[%d];'%(qn+d))\n",
    "                print('ccx q[%d], q[%d], q[%d];'%((d+2),(qn+d),(qn+d+1)))\n",
    "                print('x q[%d];'%(qn+d+1))\n",
    "                #last gate in decomposition\n",
    "                if d==qn-2:\n",
    "                    print('cu3 (%0.3f, %0.3f, %0.3f) q[%d], q[%d];'%(theta,phi,lambdaa,(qn*2-1),tg))\n",
    "                d+=1\n",
    "                lnn=[g,*lnn]\n",
    "            qfor(f,qn,lnn,d)    \n",
    "    except IndexError:\n",
    "        # the initial case qn = 0 ( >= 0 )\n",
    "        if g=='1':\n",
    "            print('id q[%d];'%tg)\n",
    "            #print('u3(%0.3f, %0.3f, %0.3f) q[%d];'%(theta,phi,lambdaa,tg))\n",
    "        # the target only has one control \n",
    "        else:\n",
    "            # control target\n",
    "            print('cu3 (%0.3f, %0.3f, %0.3f) q[%d], q[%d];'%(theta,phi,lambdaa,qn,tg))\n",
    "        qfor(f,qn,lnn) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENQASM 2.0;\n",
      "include \"qelib1.inc\";\n",
      "qreg q[6];\n",
      "id q[0];\n",
      "x q[1];\n",
      "x q[2];\n",
      "ccx q[1], q[2], q[4];\n",
      "x q[4];\n",
      "x q[3];\n",
      "x q[4];\n",
      "ccx q[3], q[4], q[5];\n",
      "x q[5];\n",
      "cu3 (0.000, 0.000, 1.571) q[5], q[0];\n",
      "x q[5];\n",
      "ccx q[3], q[4], q[5];\n",
      "x q[3];\n",
      "x q[4];\n",
      "x q[4];\n",
      "ccx q[1], q[2], q[4];\n",
      "x q[1];\n",
      "x q[2];\n",
      "x q[2];\n",
      "x q[3];\n",
      "ccx q[3], q[2], q[4];\n",
      "x q[4];\n",
      "cu3 (0.000, 0.000, 1.571) q[4], q[0];\n",
      "x q[4];\n",
      "ccx q[3], q[2], q[4];\n",
      "x q[2];\n",
      "x q[3];\n",
      "ccx q[1], q[2], q[4];\n",
      "x q[3];\n",
      "x q[4];\n",
      "ccx q[3], q[4], q[5];\n",
      "x q[5];\n",
      "cu3 (0.000, 0.000, 1.571) q[5], q[0];\n",
      "x q[5];\n",
      "ccx q[3], q[4], q[5];\n",
      "x q[3];\n",
      "x q[4];\n",
      "ccx q[1], q[2], q[4];\n",
      "cu3 (0.000, 0.000, 1.571) q[3], q[0];\n",
      "x q[1];\n",
      "x q[2];\n",
      "ccx q[1], q[2], q[4];\n",
      "x q[4];\n",
      "ccx q[3], q[4], q[5];\n",
      "cu3(0.000, 0.000, 1.571) q[5], q[0];\n",
      "ccx q[3], q[4], q[5];\n",
      "x q[4];\n",
      "ccx q[1], q[2], q[4];\n",
      "x q[1];\n",
      "x q[2];\n",
      "ccx q[3], q[2], q[4];\n",
      "cu3 (0.000, 0.000, 1.571) q[4], q[0];\n",
      "ccx q[3], q[2], q[4];\n",
      "ccx q[1], q[2], q[4];\n",
      "ccx q[3], q[4], q[5];\n",
      "cu3(0.000, 0.000, 1.571) q[5], q[0];\n",
      "ccx q[3], q[4], q[5];\n",
      "ccx q[1], q[2], q[4];\n"
     ]
    }
   ],
   "source": [
    "qfor_init('S', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "\n",
    "qfor_init('S', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output.qasm', 'w') as f:\n",
    "    f.write(cap.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc=QuantumCircuit.from_qasm_file('output.qasm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1591.6x806.68 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_matrix = execute(qc, backend)\n",
    "matrix_res = job_matrix.result()\n",
    "matrix = matrix_res.get_unitary(qc, decimals=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "final = [[]]\n",
    "for i in matrix:\n",
    "    if count<16:\n",
    "        count2 = 0\n",
    "        for j in i:\n",
    "            if count2>=16:\n",
    "                break\n",
    "            else:\n",
    "                j = round(j.real, 2) + round(j.imag, 2) * 1j\n",
    "                if j==0j:\n",
    "                    j=0\n",
    "                elif j==(1+0j):\n",
    "                    j=1\n",
    "                elif j==(-1+0j):\n",
    "                    j=-1\n",
    "                final[count].append(j)\n",
    "            count2+=1\n",
    "        if count<15:\n",
    "            final.append([])\n",
    "    else:\n",
    "        break\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1j, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, -1j, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1j, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1j]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing with the previous toolchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyzx as zx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1591.6x4599.28 with 1 Axes>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_basis = qc.decompose()\n",
    "qc_basis.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import transpile\n",
    "from qiskit.visualization import plot_circuit_layout\n",
    "from qiskit.test.mock import FakeMelbourne\n",
    "backend = FakeMelbourne()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "strqasm = QuantumCircuit.qasm(qc_basis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "print(strqasm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('strqasm.qasm', 'w') as f:\n",
    "    f.write(cap.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_qc = zx.Circuit.load(\"strqasm.qasm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ZPhase(0,phase=20854934/83408921), ZPhase(1,phase=1), XPhase(1,phase=1/2), ZPhase(1,phase=0), XPhase(1,phase=1/2), ZPhase(1,phase=1), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), T(4), HAD(4), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), T(5), HAD(5), ZPhase(5,phase=1), XPhase(5,phase=1/2), ZPhase(5,phase=0), XPhase(5,phase=1/2), ZPhase(5,phase=1), ZPhase(5,phase=20854934/83408921), CNOT(5,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(5,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), ZPhase(5,phase=1), XPhase(5,phase=1/2), ZPhase(5,phase=0), XPhase(5,phase=1/2), ZPhase(5,phase=1), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), ZPhase(1,phase=1), XPhase(1,phase=1/2), ZPhase(1,phase=0), XPhase(1,phase=1/2), ZPhase(1,phase=1), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), T(4), HAD(4), HAD(4), CNOT(2,4), T*(4), CNOT(3,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(3,4), CNOT(3,2), T*(2), T(3), CNOT(3,2), T(4), HAD(4), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), ZPhase(4,phase=20854934/83408921), CNOT(4,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(4,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), HAD(4), CNOT(2,4), T*(4), CNOT(3,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(3,4), CNOT(3,2), T*(2), T(3), CNOT(3,2), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), T(4), HAD(4), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), T(4), HAD(4), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), T(5), HAD(5), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), T(5), HAD(5), ZPhase(5,phase=1), XPhase(5,phase=1/2), ZPhase(5,phase=0), XPhase(5,phase=1/2), ZPhase(5,phase=1), ZPhase(5,phase=20854934/83408921), CNOT(5,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(5,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), ZPhase(5,phase=1), XPhase(5,phase=1/2), ZPhase(5,phase=0), XPhase(5,phase=1/2), ZPhase(5,phase=1), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), ZPhase(3,phase=1), XPhase(3,phase=1/2), ZPhase(3,phase=0), XPhase(3,phase=1/2), ZPhase(3,phase=1), ZPhase(3,phase=20854934/83408921), CNOT(3,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(3,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), ZPhase(1,phase=1), XPhase(1,phase=1/2), ZPhase(1,phase=0), XPhase(1,phase=1/2), ZPhase(1,phase=1), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), T(4), HAD(4), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), T(4), HAD(4), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), T(5), HAD(5), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), T(5), HAD(5), ZPhase(5,phase=20854934/83408921), CNOT(5,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(5,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), ZPhase(4,phase=1), XPhase(4,phase=1/2), ZPhase(4,phase=0), XPhase(4,phase=1/2), ZPhase(4,phase=1), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), ZPhase(1,phase=1), XPhase(1,phase=1/2), ZPhase(1,phase=0), XPhase(1,phase=1/2), ZPhase(1,phase=1), ZPhase(2,phase=1), XPhase(2,phase=1/2), ZPhase(2,phase=0), XPhase(2,phase=1/2), ZPhase(2,phase=1), T(4), HAD(4), HAD(4), CNOT(2,4), T*(4), CNOT(3,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(3,4), CNOT(3,2), T*(2), T(3), CNOT(3,2), T(4), HAD(4), ZPhase(4,phase=20854934/83408921), CNOT(4,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(4,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), ZPhase(0,phase=20854934/83408921), HAD(4), CNOT(2,4), T*(4), CNOT(3,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(3,4), CNOT(3,2), T*(2), T(3), CNOT(3,2), T(4), HAD(4), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), T(4), HAD(4), T(5), HAD(5), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), T(5), HAD(5), ZPhase(5,phase=20854934/83408921), CNOT(5,0), ZPhase(0,phase=-20854934/83408921), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), CNOT(5,0), ZPhase(0,phase=0), XPhase(0,phase=1/2), ZPhase(0,phase=1), XPhase(0,phase=1/2), ZPhase(0,phase=1), HAD(5), CNOT(4,5), T*(5), CNOT(3,5), T(5), CNOT(4,5), T(4), T*(5), CNOT(3,5), CNOT(3,4), T(3), T*(4), CNOT(3,4), HAD(4), CNOT(2,4), T*(4), CNOT(1,4), T(4), CNOT(2,4), T(2), T*(4), CNOT(1,4), CNOT(1,2), T(1), T*(2), CNOT(1,2), T(4), HAD(4), T(5), HAD(5)]\n"
     ]
    }
   ],
   "source": [
    "print(my_qc.gates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CNOT(3,4), CNOT(4,3), CNOT(3,4), CNOT(1,5), CNOT(5,1), CNOT(1,5), CNOT(0,4), CNOT(4,0), CNOT(0,4), HAD(5), HAD(4), HAD(2), HAD(0), ZPhase(2,phase=1), HAD(2), CZ(2,3), HAD(3), CZ(3,5), ZPhase(5,phase=1), HAD(5), CZ(3,5), ZPhase(3,phase=1/4), HAD(3), CZ(3,5), ZPhase(5,phase=3/4), HAD(5), CZ(2,5), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(5), ZPhase(5,phase=1/4), HAD(5), CZ(1,5), HAD(1), CZ(0,1), ZPhase(0,phase=1), HAD(0), CZ(0,1), ZPhase(1,phase=5/4), HAD(1), CZ(0,1), ZPhase(0,phase=7/4), HAD(0), CZ(0,5), ZPhase(5,phase=1/4), HAD(5), CNOT(5,1), HAD(0), ZPhase(0,phase=7/4), HAD(0), CNOT(0,1), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,1), HAD(0), HAD(4), CZ(0,4), ZPhase(0,phase=5/4), HAD(0), CZ(0,4), ZPhase(4,phase=20854934/83408921), HAD(4), CZ(0,4), ZPhase(0,phase=41699053/166817842), HAD(5), HAD(0), HAD(4), CZ(4,5), CZ(0,4), ZPhase(4,phase=291936631/166817842), HAD(4), ZPhase(4,phase=3/4), HAD(4), CNOT(4,1), HAD(4), ZPhase(4,phase=1/4), HAD(4), CZ(4,5), ZPhase(5,phase=5/4), HAD(5), CNOT(5,1), ZPhase(2,phase=1/4), HAD(2), CNOT(2,3), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,3), HAD(2), CNOT(2,1), HAD(5), CZ(2,5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,1), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(5), ZPhase(5,phase=1/4), HAD(5), CZ(2,5), ZPhase(2,phase=1/4), HAD(2), CNOT(2,3), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(2), ZPhase(2,phase=3/4), HAD(2), CNOT(2,3), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,1), HAD(5), ZPhase(5,phase=1/4), HAD(5), HAD(2), CZ(2,5), HAD(5), CZ(1,5), HAD(1), CZ(1,5), ZPhase(5,phase=1/4), HAD(5), CZ(1,5), ZPhase(1,phase=3/4), HAD(1), CZ(1,2), ZPhase(2,phase=5/4), HAD(2), CNOT(2,5), HAD(1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,5), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,5), HAD(0), HAD(1), ZPhase(0,phase=125118789/166817842), ZPhase(1,phase=5/4), HAD(0), HAD(1), CZ(0,1), HAD(0), CZ(0,1), ZPhase(0,phase=208516895/166817842), HAD(2), HAD(0), CZ(1,2), CZ(0,1), ZPhase(1,phase=291936631/166817842), HAD(1), ZPhase(1,phase=3/4), HAD(1), CNOT(1,5), HAD(1), ZPhase(1,phase=1/4), HAD(1), CZ(1,2), ZPhase(2,phase=5/4), HAD(2), CNOT(2,5), HAD(1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,5), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,5), HAD(1), ZPhase(1,phase=1/4), ZPhase(2,phase=1), HAD(1), HAD(2), CZ(1,2), HAD(1), CZ(1,3), ZPhase(3,phase=1), HAD(3), CZ(1,3), ZPhase(1,phase=5/4), HAD(1), CZ(1,3), ZPhase(3,phase=7/4), HAD(3), CZ(2,3), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,1), HAD(4), HAD(3), ZPhase(4,phase=1/4), ZPhase(3,phase=5/4), HAD(4), HAD(3), CZ(3,4), HAD(4), CZ(4,5), HAD(5), CZ(4,5), ZPhase(4,phase=1/4), HAD(4), CZ(4,5), ZPhase(5,phase=3/4), HAD(5), CZ(3,5), ZPhase(3,phase=1/4), HAD(3), CNOT(3,4), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,4), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,4), HAD(0), HAD(5), ZPhase(0,phase=125118789/166817842), ZPhase(5,phase=5/4), HAD(0), HAD(5), CZ(0,5), HAD(0), CZ(0,5), ZPhase(0,phase=208516895/166817842), HAD(3), HAD(0), CZ(3,5), CZ(0,5), ZPhase(5,phase=291936631/166817842), HAD(5), ZPhase(5,phase=3/4), HAD(5), CNOT(5,4), HAD(5), ZPhase(5,phase=1/4), HAD(5), CZ(3,5), ZPhase(3,phase=5/4), HAD(3), CNOT(3,4), ZPhase(2,phase=1/4), HAD(2), CNOT(2,1), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,1), HAD(2), CNOT(2,4), HAD(3), CZ(2,3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,4), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,4), ZPhase(3,phase=3/4), HAD(3), CNOT(3,1), HAD(3), ZPhase(3,phase=1/4), HAD(3), CZ(2,3), ZPhase(2,phase=5/4), HAD(2), CNOT(2,1), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,1), HAD(2), ZPhase(2,phase=3/4), HAD(2), CNOT(2,1), HAD(3), ZPhase(3,phase=1/4), ZPhase(2,phase=1), HAD(3), HAD(2), CZ(2,3), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,1), ZPhase(3,phase=1), HAD(3), ZPhase(3,phase=1/4), HAD(3), CZ(2,3), ZPhase(2,phase=5/4), HAD(2), CNOT(2,1), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,1), ZPhase(2,phase=1), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,1), ZPhase(3,phase=1), HAD(5), HAD(3), ZPhase(5,phase=1/4), ZPhase(3,phase=5/4), HAD(5), HAD(3), CZ(3,5), HAD(5), ZPhase(5,phase=7/4), ZPhase(4,phase=1), HAD(5), HAD(4), CZ(4,5), HAD(0), HAD(5), ZPhase(0,phase=125118789/166817842), ZPhase(5,phase=1/4), HAD(0), HAD(5), CZ(3,5), CZ(0,4), HAD(0), HAD(5), CZ(0,4), ZPhase(0,phase=208516895/166817842), ZPhase(5,phase=7/4), HAD(0), HAD(5), CZ(4,5), CZ(0,4), ZPhase(3,phase=1/4), HAD(3), HAD(0), HAD(5), CZ(3,4), ZPhase(0,phase=125118789/166817842), ZPhase(5,phase=1/4), HAD(3), HAD(0), HAD(5), CZ(0,5), ZPhase(3,phase=7/4), HAD(3), HAD(0), CZ(3,4), CZ(0,5), ZPhase(0,phase=208516895/166817842), HAD(3), HAD(0), CZ(3,5), CZ(0,5), ZPhase(5,phase=291936631/166817842), HAD(5), ZPhase(5,phase=7/4), HAD(5), CZ(4,5), HAD(5), ZPhase(5,phase=1/4), HAD(5), CZ(3,5), ZPhase(3,phase=1/4), HAD(3), CZ(3,4), ZPhase(2,phase=1), HAD(3), HAD(2), CZ(2,3), ZPhase(3,phase=7/4), HAD(3), CZ(3,4), ZPhase(3,phase=3/4), HAD(3), CNOT(3,1), ZPhase(3,phase=1), HAD(3), ZPhase(3,phase=1/4), HAD(3), CZ(2,4), CZ(2,3), ZPhase(2,phase=5/4), HAD(2), CNOT(2,1), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,1), ZPhase(2,phase=1), HAD(2), ZPhase(2,phase=3/4), HAD(2), CNOT(2,1), ZPhase(3,phase=1), HAD(3), ZPhase(3,phase=1/4), HAD(3), HAD(2), CZ(2,3), ZPhase(4,phase=20854934/83408921), HAD(3), HAD(4), CZ(3,4), ZPhase(3,phase=7/4), HAD(3), HAD(4), CZ(3,4), ZPhase(4,phase=1/4), HAD(4), CZ(2,4), ZPhase(2,phase=5/4), HAD(2), CNOT(2,3), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,3), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,3), HAD(0), HAD(4), ZPhase(0,phase=125118789/166817842), ZPhase(4,phase=1/4), HAD(0), HAD(4), CZ(0,4), HAD(0), CZ(0,4), ZPhase(0,phase=208516895/166817842), HAD(2), HAD(0), CZ(2,4), CZ(0,4), ZPhase(4,phase=291936631/166817842), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,3), HAD(4), ZPhase(4,phase=1/4), HAD(4), CZ(2,4), ZPhase(2,phase=1/4), HAD(2), CNOT(2,3), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,3), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,3), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(4), ZPhase(4,phase=1/4), HAD(4), HAD(2), CZ(2,4), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,1), HAD(4), ZPhase(4,phase=1/4), HAD(4), CZ(2,4), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,1), HAD(5), HAD(4), ZPhase(5,phase=1/4), ZPhase(4,phase=1/4), HAD(5), HAD(4), CZ(4,5), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(5), ZPhase(5,phase=1/4), HAD(5), CZ(4,5), ZPhase(4,phase=1/4), HAD(4), CNOT(4,3), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,3), HAD(0), HAD(5), ZPhase(0,phase=125118789/166817842), ZPhase(5,phase=1/4), HAD(0), HAD(5), CZ(0,5), HAD(0), ZPhase(0,phase=145962908/83408921), HAD(4), HAD(0), CZ(4,5), CZ(0,5), ZPhase(5,phase=20854934/83408921), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), HAD(5), ZPhase(5,phase=1/4), HAD(5), CNOT(5,3), CZ(4,5), ZPhase(4,phase=1/4), HAD(4), CNOT(4,3), HAD(5), ZPhase(5,phase=1/4), HAD(5), CNOT(5,3), ZPhase(2,phase=1/4), HAD(2), CNOT(2,1), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,1), HAD(2), CNOT(2,3), HAD(4), CZ(2,4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,3), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), ZPhase(4,phase=7/4), HAD(4), CNOT(4,1), HAD(4), ZPhase(4,phase=1/4), HAD(4), CNOT(4,1), CZ(2,4), HAD(4), ZPhase(4,phase=1/4), HAD(4), CNOT(4,1), ZPhase(2,phase=1/4), HAD(2), CNOT(2,1), HAD(4), ZPhase(4,phase=7/4), HAD(4), CNOT(4,1), HAD(2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,1), HAD(3), HAD(2), HAD(1), HAD(0)]\n"
     ]
    }
   ],
   "source": [
    "c = c.to_basic_gates()\n",
    "print(c.gates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit  on 6 qubits with 594 gates.\n",
      "        141 is the T-count\n",
      "        453 Cliffords among which \n",
      "        169 2-qubit gates (83 CNOT, 86 other) and\n",
      "        270 Hadamard gates.\n"
     ]
    }
   ],
   "source": [
    "print(c.stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph(775 vertices, 938 edges)\n"
     ]
    }
   ],
   "source": [
    "g = c.to_graph()\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph(116 vertices, 324 edges)\n"
     ]
    }
   ],
   "source": [
    "zx.simplify.full_reduce(g)\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit  on 6 qubits with 305 gates.\n",
      "        59 is the T-count\n",
      "        246 Cliffords among which \n",
      "        138 2-qubit gates (91 CNOT, 47 other) and\n",
      "        106 Hadamard gates.\n"
     ]
    }
   ],
   "source": [
    "c2 = zx.extract_circuit(g).to_basic_gates()\n",
    "print(c2.stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "print(c2.to_qasm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final1.qasm', 'w') as f:\n",
    "    f.write(cap.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1591.6x5020.68 with 1 Axes>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal=QuantumCircuit.from_qasm_file('final1.qasm')\n",
    "\n",
    "qcfinal.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_qc2 = zx.Circuit.from_quipper_file(\"quipper.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[HAD(0), HAD(1), HAD(2), HAD(4), CNOT(4,0), CNOT(0,1), T(0), T*(1), CNOT(4,0), CNOT(0,1), T*(4), T(1), CNOT(4,1), HAD(4), HAD(5), CNOT(5,2), CNOT(2,4), T(2), T*(4), CNOT(5,2), CNOT(2,4), T*(5), T(4), CNOT(5,4), HAD(5), T*(3), T*(5), CNOT(3,5), T(5), CNOT(3,5), HAD(5), CNOT(5,4), T*(4), T(5), CNOT(2,4), CNOT(5,2), T(4), T*(2), CNOT(2,4), CNOT(5,2), HAD(5), HAD(4), CNOT(4,1), T*(1), T(4), CNOT(0,1), CNOT(4,0), T(1), T*(0), CNOT(0,1), CNOT(4,0), HAD(4), HAD(4), CNOT(4,0), CNOT(0,1), T(0), T*(1), CNOT(4,0), CNOT(0,1), T*(4), T(1), CNOT(4,1), HAD(4), HAD(5), CNOT(5,2), CNOT(2,4), T*(2), T(4), CNOT(5,2), CNOT(2,4), T*(5), T(4), CNOT(5,4), HAD(5), T*(3), T*(5), CNOT(3,5), T(5), CNOT(3,5), HAD(5), CNOT(5,4), T*(4), T(5), CNOT(2,4), CNOT(5,2), T*(4), T(2), CNOT(2,4), CNOT(5,2), HAD(5), HAD(4), CNOT(4,1), T*(1), T(4), CNOT(0,1), CNOT(4,0), T(1), T*(0), CNOT(0,1), CNOT(4,0), HAD(4), HAD(4), CNOT(4,0), CNOT(0,1), T(0), T(1), CNOT(4,0), CNOT(0,1), T*(4), T*(1), CNOT(4,1), HAD(4), HAD(5), CNOT(5,2), CNOT(2,4), T(2), T*(4), CNOT(5,2), CNOT(2,4), T*(5), T(4), CNOT(5,4), HAD(5), CNOT(3,5), T*(5), CNOT(3,5), T(5), T(3), HAD(5), CNOT(5,4), T*(4), T(5), CNOT(2,4), CNOT(5,2), T(4), T*(2), CNOT(2,4), CNOT(5,2), HAD(5), HAD(4), CNOT(4,1), T(1), T(4), CNOT(0,1), CNOT(4,0), T*(1), T*(0), CNOT(0,1), CNOT(4,0), HAD(4), HAD(4), CNOT(4,0), CNOT(0,1), T*(0), T(1), CNOT(4,0), CNOT(0,1), T*(4), T(1), CNOT(4,1), HAD(4), HAD(5), CNOT(5,2), CNOT(2,4), T(2), T*(4), CNOT(5,2), CNOT(2,4), T*(5), T(4), CNOT(5,4), HAD(5), T*(3), T*(5), CNOT(3,5), T(5), CNOT(3,5), HAD(5), CNOT(5,4), T*(4), T(5), CNOT(2,4), CNOT(5,2), T(4), T*(2), CNOT(2,4), CNOT(5,2), HAD(5), HAD(4), CNOT(4,1), T*(1), T(4), CNOT(0,1), CNOT(4,0), T*(1), T(0), CNOT(0,1), CNOT(4,0), HAD(4), HAD(4), CNOT(4,0), CNOT(0,1), T*(0), T(1), CNOT(4,0), CNOT(0,1), T*(4), T(1), CNOT(4,1), HAD(4), T(3), T*(2), T(4), CNOT(2,4), CNOT(3,2), CNOT(4,3), T(2), T*(3), CNOT(4,2), T*(2), T(4), CNOT(3,2), CNOT(4,3), CNOT(2,4), HAD(4), CNOT(4,1), T*(1), T(4), CNOT(0,1), CNOT(4,0), T*(1), T(0), CNOT(0,1), CNOT(4,0), HAD(4), HAD(4), CNOT(4,0), CNOT(0,1), T*(0), T*(1), CNOT(4,0), CNOT(0,1), T*(4), T*(1), CNOT(4,1), HAD(4), HAD(5), CNOT(5,2), CNOT(2,4), T(2), T*(4), CNOT(5,2), CNOT(2,4), T*(5), T(4), CNOT(5,4), HAD(5), CNOT(3,5), T*(5), CNOT(3,5), T(5), T(3), HAD(5), CNOT(5,4), T*(4), T(5), CNOT(2,4), CNOT(5,2), T(4), T*(2), CNOT(2,4), CNOT(5,2), HAD(5), HAD(4), CNOT(4,1), T(1), T(4), CNOT(0,1), CNOT(4,0), T(1), T(0), CNOT(0,1), CNOT(4,0), HAD(4)]\n"
     ]
    }
   ],
   "source": [
    "print(my_qc2.gates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CNOT(4,5), CNOT(5,4), CNOT(4,5), CNOT(1,5), CNOT(5,1), CNOT(1,5), CNOT(0,5), CNOT(5,0), CNOT(0,5), HAD(5), HAD(3), HAD(2), HAD(0), HAD(0), CZ(0,1), HAD(1), CZ(1,2), HAD(2), CNOT(2,0), ZPhase(4,phase=1), HAD(4), CZ(3,4), ZPhase(1,phase=1/4), HAD(1), HAD(3), HAD(5), CZ(2,5), CZ(2,4), CZ(2,3), CZ(1,2), CZ(0,2), ZPhase(2,phase=7/4), HAD(2), CZ(0,5), CZ(0,4), CZ(0,3), CZ(0,2), CZ(0,1), ZPhase(0,phase=7/4), HAD(0), HAD(1), CZ(1,3), CZ(0,1), ZPhase(1,phase=1/4), HAD(1), CZ(3,4), CZ(1,4), ZPhase(4,phase=3/4), HAD(4), CZ(1,5), CZ(1,4), ZPhase(1,phase=3/4), HAD(1), CZ(3,5), CZ(3,4), CZ(1,3), ZPhase(3,phase=5/4), HAD(3), HAD(4), CZ(3,4), ZPhase(4,phase=1/4), HAD(4), CNOT(4,5), CZ(4,5), ZPhase(4,phase=125118789/166817842), HAD(4), CZ(4,5), ZPhase(5,phase=41699053/166817842), HAD(5), CNOT(5,1), CNOT(1,3), CNOT(5,0), ZPhase(5,phase=5/4), HAD(5), CNOT(5,1), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,3), CNOT(5,1), CNOT(3,2), CNOT(3,4), ZPhase(3,phase=5/4), HAD(3), CNOT(3,2), CNOT(3,1), HAD(3), ZPhase(3,phase=7/4), HAD(3), CNOT(3,0), CNOT(0,2), CNOT(2,1), HAD(3), CZ(2,3), ZPhase(3,phase=1/4), HAD(3), HAD(2), CZ(2,3), ZPhase(2,phase=7/4), HAD(2), CNOT(2,4), ZPhase(2,phase=208516895/166817842), HAD(2), CNOT(2,4), HAD(2), ZPhase(2,phase=291936631/166817842), HAD(2), CNOT(2,1), CNOT(2,5), CNOT(2,0), ZPhase(2,phase=1/4), HAD(2), CNOT(2,3), CNOT(2,1), CNOT(2,0), HAD(2), ZPhase(2,phase=1/4), HAD(2), CNOT(2,3), CNOT(3,1), CNOT(3,0), HAD(2), CZ(2,3), ZPhase(2,phase=7/4), HAD(2), HAD(3), CZ(2,3), ZPhase(3,phase=3/4), HAD(3), CNOT(3,1), CNOT(3,5), CNOT(3,0), ZPhase(3,phase=1/4), HAD(3), CNOT(3,1), HAD(3), CZ(3,5), ZPhase(3,phase=7/4), HAD(3), CNOT(3,4), CNOT(3,1), CZ(3,4), ZPhase(3,phase=41699053/166817842), HAD(3), HAD(4), CZ(3,4), ZPhase(4,phase=291936631/166817842), HAD(4), CNOT(4,0), CNOT(4,5), ZPhase(4,phase=5/4), HAD(4), CNOT(4,1), CNOT(4,0), HAD(4), ZPhase(4,phase=1/4), HAD(4), HAD(1), CZ(1,4), ZPhase(1,phase=20854934/83408921), HAD(1), CNOT(1,0), HAD(4), CZ(1,4), ZPhase(4,phase=5/4), HAD(4), HAD(1), CZ(1,4), ZPhase(1,phase=1/4), HAD(1), CNOT(1,3), CNOT(1,0), ZPhase(1,phase=291936631/166817842), HAD(1), CNOT(1,3), HAD(1), ZPhase(1,phase=41699053/166817842), HAD(1), CNOT(1,2), CNOT(1,4), ZPhase(1,phase=1/4), HAD(1), CNOT(1,0), CNOT(1,4), HAD(1), CZ(1,5), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), CNOT(1,5), CNOT(5,0), ZPhase(1,phase=1/4), HAD(1), CNOT(1,2), HAD(1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), CNOT(1,4), HAD(1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), HAD(1), ZPhase(1,phase=5/4), HAD(1), CNOT(1,2), CNOT(1,3), ZPhase(1,phase=291936631/166817842), HAD(1), CNOT(1,3), HAD(1), ZPhase(1,phase=41699053/166817842), HAD(1), CNOT(1,2), CNOT(1,3), CNOT(1,5), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), CNOT(1,4), HAD(1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), HAD(1), ZPhase(1,phase=5/4), HAD(1), CNOT(1,2), CNOT(2,0), CNOT(1,4), HAD(1), ZPhase(1,phase=1/4), HAD(1), CNOT(1,0), ZPhase(1,phase=7/4), HAD(1), CNOT(1,0), CNOT(1,4), HAD(1), ZPhase(1,phase=1/4), HAD(1), CNOT(1,0), CNOT(1,4), CNOT(1,5), ZPhase(5,phase=145962908/83408921), HAD(5), CNOT(5,3), CNOT(3,0), CNOT(3,4), HAD(5), ZPhase(5,phase=20854934/83408921), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,1), HAD(5), ZPhase(5,phase=7/4), HAD(5), CNOT(5,0), CNOT(5,4), CNOT(5,1), HAD(5), ZPhase(5,phase=1/4), HAD(5), CNOT(5,1), HAD(5), ZPhase(5,phase=1/4), HAD(5), CNOT(5,1), ZPhase(1,phase=7/4), HAD(1), CNOT(1,2), HAD(1), ZPhase(1,phase=1/4), HAD(1), CNOT(1,0), CNOT(0,4), HAD(1), CZ(1,4), CZ(1,2), ZPhase(1,phase=5/4), HAD(1), HAD(2), CZ(1,2), ZPhase(2,phase=7/4), HAD(2), CNOT(2,4), HAD(3), CZ(3,5), CZ(3,4), CZ(2,3), CZ(0,3), ZPhase(3,phase=208516895/166817842), HAD(3), HAD(0), CZ(0,5), CZ(0,4), CZ(0,3), CZ(0,2), ZPhase(5,phase=1), HAD(3), HAD(2), HAD(1), ZPhase(0,phase=62575617/83408921)]\n"
     ]
    }
   ],
   "source": [
    "c2 = c2.to_basic_gates()\n",
    "print(c2.gates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit  on 6 qubits with 305 gates.\n",
      "        59 is the T-count\n",
      "        246 Cliffords among which \n",
      "        138 2-qubit gates (91 CNOT, 47 other) and\n",
      "        106 Hadamard gates.\n"
     ]
    }
   ],
   "source": [
    "print(c2.stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph(455 vertices, 587 edges)\n"
     ]
    }
   ],
   "source": [
    "g2 = c2.to_graph()\n",
    "print(g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph(115 vertices, 305 edges)\n"
     ]
    }
   ],
   "source": [
    "zx.simplify.full_reduce(g2)\n",
    "print(g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit  on 6 qubits with 293 gates.\n",
      "        59 is the T-count\n",
      "        234 Cliffords among which \n",
      "        126 2-qubit gates (86 CNOT, 40 other) and\n",
      "        106 Hadamard gates.\n"
     ]
    }
   ],
   "source": [
    "c22 = zx.extract_circuit(g2).to_basic_gates()\n",
    "print(c22.stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "print(c22.to_qasm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final2.qasm', 'w') as f:\n",
    "    f.write(cap.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1591.6x4599.28 with 1 Axes>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal2=QuantumCircuit.from_qasm_file('final2.qasm')\n",
    "\n",
    "qcfinal2.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal2.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal.width()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal2.width()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('h', 106), ('cx', 91), ('rz', 61), ('cz', 47)])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal.count_ops()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('h', 106), ('cx', 86), ('rz', 61), ('cz', 40)])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcfinal2.count_ops()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "The original experiments employ a toolchain that always resorts to the semantics of the quantamorphism (unitary matrix) to compile the final circuit, via Quipper. This step in the toolchain cripples the prospect of building a scalable compilation scheme.\n",
    "\n",
    "The alternative (direct) compilation scheme aims to eradicate the unitary matrix from the compilation process.\n",
    "\n",
    "It turns out that the two circuits end up being incredibly similar. However, the original toolchain returns one with slightly smaller depth, thanks to simplifications made by Quipper's exact_synthesis function. In the direct approach optimization was not a priority, and the pyzx compiler was not enough to mitigate the consequences of this decision.\n",
    "\n",
    "Despite the clear room for progress, the experiment shows how to perform direct compilation of a quantum for-loop with a shorter toolchain dispensing with the explicit construction of the unitary matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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