03-parallel-processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallel construction of circuits\n",
    "\n",
    "In this notebook we try to prepare all the circuits beforehand, and then simulate them all at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from qiskit import (\n",
    "    QuantumCircuit,\n",
    "    ClassicalRegister,\n",
    "    QuantumRegister,\n",
    "    execute,\n",
    "    Aer,\n",
    "    BasicAer\n",
    "    )\n",
    "from qiskit.tools.visualization import plot_histogram, plot_state_city, plot_bloch_multivector\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import qiskit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "q = QuantumRegister(1,\"q\")\n",
    "c = ClassicalRegister(1,\"c\")\n",
    "circuit = QuantumCircuit(q,c)\n",
    "circuit.h(q)\n",
    "circuit.measure(q,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "backend = BasicAer.get_backend('statevector_simulator')\n",
    "job = execute(circuit, backend)\n",
    "result = job.result()\n",
    "output_state = result.get_statevector(circuit, decimals=2)\n",
    "print(\"state vector after circuit:\", output_state)\n",
    "print(circuit.draw())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "job = execute(circuit, simulator, shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts(circuit)\n",
    "print(\"\\nTotal counts are:\",counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "qc_list = [circuit,circuit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Add to your import statements\n",
    "from qiskit.compiler import transpile, assemble\n",
    "\n",
    "# After creating qc_list\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "transpiled_circs = transpile(qc_list, backend=backend)\n",
    "qobjs = assemble(transpiled_circs, backend=backend)\n",
    "job_info = backend.run(qobjs)\n",
    "\n",
    "# To get the results\n",
    "for circ_index in range(len(transpiled_circs)):\n",
    "    print(job_info.result().get_counts(transpiled_circs[circ_index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from qiskit import (\n",
    "    QuantumCircuit,\n",
    "    ClassicalRegister,\n",
    "    QuantumRegister,\n",
    "    execute,\n",
    "    Aer,\n",
    "    BasicAer\n",
    "    )\n",
    "from qiskit.tools.visualization import plot_histogram, plot_state_city, plot_bloch_multivector\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import qiskit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qiskit-terra': '0.10.0',\n",
       " 'qiskit-aer': '0.3.2',\n",
       " 'qiskit-ignis': '0.2.0',\n",
       " 'qiskit-ibmq-provider': '0.3.3',\n",
       " 'qiskit-aqua': '0.6.1',\n",
       " 'qiskit': '0.13.0'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qiskit.__qiskit_version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definizione dei sign flip\n",
    "\n",
    "$$SF_{N,j} = O_j (C^NZ) O_j$$\n",
    "\n",
    "$$O_j = \\otimes_{l=0}^{N-1} (NOT_l)^{1-j_l}$$\n",
    "\n",
    "Facciamo $N=3$\n",
    "\n",
    "$j=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "N=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputVector = np.zeros(2**N)\n",
    "inputVector[0] = 1\n",
    "inputVector\n",
    "# weightVector = [0,0,0,0,0,1,0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "j rappresenta gli elementi della base computazionale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [1., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [1., 0., 1., 0.],\n",
       "       [0., 1., 1., 0.],\n",
       "       [1., 1., 1., 0.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [1., 0., 0., 1.],\n",
       "       [0., 1., 0., 1.],\n",
       "       [1., 1., 0., 1.],\n",
       "       [0., 0., 1., 1.],\n",
       "       [1., 0., 1., 1.],\n",
       "       [0., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_computazionale = np.zeros((2**N,N))\n",
    "for i in range(2**N):\n",
    "    for cifra in range(N):\n",
    "        base_computazionale[i][cifra] = format(i, '0'+str(N)+'b')[N-1-cifra]\n",
    "base_computazionale\n",
    "\n",
    "# format(2,'0'+str(N)+'b')[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "q = QuantumRegister(N, 'q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def O(circuit, N, j):\n",
    "    O_circ = QuantumCircuit(q)\n",
    "    for l in range(N):\n",
    "        if j[l] == 0:\n",
    "            O_circ.x(q[l])\n",
    "    circuit += O_circ\n",
    "\n",
    "def sf(circuit, N, j):\n",
    "    SF = QuantumCircuit(q)\n",
    "    O(SF, N, j)\n",
    "    SF.barrier(q)\n",
    "    SF.mcrz(np.pi, q[:N-1], q[N-1])\n",
    "    SF.barrier(q)\n",
    "    O(SF, N, j)\n",
    "    \n",
    "    circuit += SF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def U_i(circuit, inputVector):\n",
    "    \"\"\"\n",
    "    Funione per codificare il vettore d'input.\n",
    "    Da chiamare dentro il circuito principale.\n",
    "    \"\"\"\n",
    "    u_i = QuantumCircuit(q)\n",
    "    u_i.h(q)\n",
    "    for (index, value) in enumerate(inputVector):\n",
    "        # print(\"U_i, passo \" + str(index))\n",
    "        if inputVector[index]: sf(u_i, N, base_computazionale[index])\n",
    "    circuit += u_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def U_w(circuit, weightVector):\n",
    "    \"\"\"\n",
    "    Funione per codificare il vettore d'input.\n",
    "    Da chiamare dentro il circuito principale.\n",
    "    \"\"\"\n",
    "    u_w = QuantumCircuit(q)\n",
    "    for (index, value) in enumerate(weightVector):\n",
    "        # print(\"U_w, passo \" + str(index))\n",
    "        if weightVector[index]: sf(u_w, N, base_computazionale[index])\n",
    "    u_w.h(q)\n",
    "    u_w.x(q)\n",
    "    circuit += u_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "circ.draw(output='mpl')\n",
    "# mcrz(self, lam, q_controls, q_target, use_basis_gates=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def draw_state_vector(circ):\n",
    "    \"\"\"\n",
    "        Input: \n",
    "            - circ (qiskit.circuit.quantumcircuit.QuantumCircuit) quantum circuit \n",
    "              to be executed\n",
    "        Description:\n",
    "            - Runs the quantum circuit and prints the resulting quantum state vector \n",
    "              and draws the circuit.\n",
    "    \"\"\"\n",
    "    \n",
    "    backend = BasicAer.get_backend('statevector_simulator')\n",
    "    job = execute(circ, backend)\n",
    "    result = job.result()\n",
    "    output_state = result.get_statevector(circ, decimals=2)\n",
    "    print(\"state vector after circuit:\", output_state)\n",
    "    print(circ.draw())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inputVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inputVector = [0,0,0,0,0,0,0,1]\n",
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "print(inputVector)\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inputVector = [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]\n",
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "print(inputVector)\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inputVector = [0,0,0,0,0,1,0,0]\n",
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "print(inputVector)\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "print(inputVector)\n",
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "print(inputVector)\n",
    "circ = QuantumCircuit(q)\n",
    "U_i(circ, inputVector)\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Esecuzione del circuito completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inputVector = [1.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.0]\n",
    "\n",
    "weightVector = [0.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0,\n",
    " 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def numtovect(k,N):\n",
    "    m = 2**N\n",
    "    v = np.zeros(m)\n",
    "    binary_string = (\"{:0%db}\"%m).format(k)\n",
    "    #convert k to an m-digit binary number\n",
    "    #print(binary_string)\n",
    "    v = list(map(lambda v, b : v**int(b), v, binary_string)) \n",
    "    return np.array(v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "q = QuantumRegister(N, 'q')\n",
    "a = QuantumRegister(1, 'a')\n",
    "b = ClassicalRegister(1, 'b')\n",
    "circ = QuantumCircuit(q,a,b)\n",
    "U_i(circ, numtovect(3,N))\n",
    "U_w(circ, weightVector)\n",
    "# (theta, q_controls, q_target, use_basis_gates=False)\n",
    "circ.mcrx(np.pi, q, a[0])\n",
    "circ.measure(a, b)\n",
    "\n",
    "draw_state_vector(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "q = QuantumRegister(N, 'q')\n",
    "a = QuantumRegister(1, 'a')\n",
    "b = ClassicalRegister(1, 'b')\n",
    "circ = QuantumCircuit(q,a,b)\n",
    "U_i(circ, numtovect(0,N))\n",
    "U_w(circ, numtovect(4, N))\n",
    "# (theta, q_controls, q_target, use_basis_gates=False)\n",
    "circ.mcrx(np.pi, q, a[0])\n",
    "circ.measure(a, b)\n",
    "\n",
    "# draw_state_vector(circ)\n",
    "\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "job = execute(circ, simulator, shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts(circ)\n",
    "print(\"\\nTotal counts are:\",counts)\n",
    "# plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "job = execute(circ, simulator, shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts(circ)\n",
    "print(\"\\nTotal counts are:\",counts)\n",
    "# plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "counts.get('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "numtovect(0,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "datetime.now().time().minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "datetime.timedelta(minutes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "start = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "end = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "end - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19:41:07.021549\n",
      "Vettore d'input #0\n",
      "Vettore d'input #1\n",
      "Vettore d'input #2\n",
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      "Vettore d'input #216\n",
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      "Vettore d'input #221\n",
      "Vettore d'input #222\n",
      "Vettore d'input #223\n",
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      "Vettore d'input #225\n",
      "Vettore d'input #226\n",
      "Vettore d'input #227\n",
      "Vettore d'input #228\n",
      "Vettore d'input #229\n",
      "Vettore d'input #230\n",
      "Vettore d'input #231\n",
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      "Vettore d'input #233\n",
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      "Vettore d'input #235\n",
      "Vettore d'input #236\n",
      "Vettore d'input #237\n",
      "Vettore d'input #238\n",
      "Vettore d'input #239\n",
      "Vettore d'input #240\n",
      "Vettore d'input #241\n",
      "Vettore d'input #242\n",
      "Vettore d'input #243\n",
      "Vettore d'input #244\n",
      "Vettore d'input #245\n",
      "Vettore d'input #246\n",
      "Vettore d'input #247\n",
      "Vettore d'input #248\n",
      "Vettore d'input #249\n",
      "Vettore d'input #250\n",
      "Vettore d'input #251\n",
      "Vettore d'input #252\n",
      "Vettore d'input #253\n",
      "Vettore d'input #254\n",
      "Vettore d'input #255\n",
      "19:46:03.108175\n",
      "Tempo di costruzione: 0:04:56.086619\n",
      "Inizio l'assemblaggio di tutti i circuiti\n",
      "Tempo di esecuzione: 2:10:19.969989\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "N = 3\n",
    "\n",
    "base_computazionale = np.zeros((2**N,N))\n",
    "for i in range(2**N):\n",
    "    for cifra in range(N):\n",
    "        base_computazionale[i][cifra] = format(i, '0'+str(N)+'b')[N-1-cifra]\n",
    "base_computazionale\n",
    "\n",
    "listaCircuiti = []\n",
    "\n",
    "startTime = datetime.now()\n",
    "\n",
    "print(datetime.now().time())\n",
    "outcomes = np.zeros((2**(2**N),2**(2**N)))\n",
    "for i in range(2**(2**N)):\n",
    "    forTime0 = datetime.now()\n",
    "    print(\"Vettore d'input #\" + str(i))\n",
    "    for w in range(2**(2**N)):\n",
    "        q = QuantumRegister(N, 'q')\n",
    "        a = QuantumRegister(1, 'a')\n",
    "        b = ClassicalRegister(1, 'b')\n",
    "        circ = QuantumCircuit(q,a,b)\n",
    "        # print('Input vector: ' + str(numtovect(i,N)) + ' - Weight vector: ' + str(numtovect(w,N)))\n",
    "        U_i(circ, numtovect(i,N))\n",
    "        U_w(circ, numtovect(w, N))\n",
    "        # (theta, q_controls, q_target, use_basis_gates=False)\n",
    "        circ.mcrx(np.pi, q, a[0])\n",
    "        circ.measure(a, b)\n",
    "\n",
    "        listaCircuiti.append(circ)\n",
    "        # draw_state_vector(circ)\n",
    "\n",
    "        \n",
    "    # forTime1 = datetime.now()\n",
    "    # print(\"Tempo per un vettore d'input: \" + str(forTime1 - forTime0))\n",
    "endTime = datetime.now()\n",
    "print(datetime.now().time())\n",
    "print('Tempo di costruzione: ' + str(endTime - startTime))\n",
    "print(\"Inizio l'assemblaggio di tutti i circuiti\")\n",
    "startTime = datetime.now()\n",
    "# Add to your import statements\n",
    "from qiskit.compiler import transpile, assemble\n",
    "\n",
    "# After creating qc_list\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "transpiled_circs = transpile(listaCircuiti, backend=backend)\n",
    "qobjs = assemble(transpiled_circs, backend=backend)\n",
    "job_info = backend.run(qobjs)\n",
    "\n",
    "# To get the results\n",
    "\n",
    "for circ_index in range(len(transpiled_circs)):\n",
    "    counts = job_info.result().get_counts(transpiled_circs[circ_index])\n",
    "    if len(counts) == 1:\n",
    "        outcomes[int(int(circ_index)/int(2**(2**N)))][int(int(circ_index)%int(2**(2**N)))] = int([*counts][0]) # unpacking\n",
    "    else:\n",
    "        outcomes[int(int(circ_index)/int(2**(2**N)))][int(int(circ_index)%int(2**(2**N)))] = counts['1']/float(1024)\n",
    "endTime = datetime.now()\n",
    "print(\"Tempo di esecuzione:\", endTime-startTime)\n",
    "# simulator = Aer.get_backend('qasm_simulator')\n",
    "# job = execute(circ, simulator, shots=1024)\n",
    "# result = job.result()\n",
    "# counts = result.get_counts(circ)\n",
    "#print(\"\\nTotal counts are:\",counts)\n",
    "# plot_histogram(counts)\n",
    "# if len(counts) == 1:\n",
    "#     outcomes[i][w] = int([*counts][0]) # unpacking\n",
    "# else:\n",
    "# outcomes[i][w] = counts['1']/float(1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f5fd3c79890>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ax.matshow(outcomes, cmap=plt.cm.Blues)\n",
    "ax.set_xlabel(\"weight\")\n",
    "ax.set_ylabel(\"input\")\n",
    "\n",
    "out_plot = ax.imshow(outcomes, cmap='Blues', interpolation='none')\n",
    "fig.colorbar(out_plot, ax=ax)\n",
    "\n",
    "# fig.savefig('inputs_v_weights.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "filename = 'citymap_N3.pkl'\n",
    "dill.dump_session(filename)\n",
    "\n",
    "# and to load the session again:\n",
    "# dill.load_session(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "(numtovect(34,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "N = 3\n",
    "vectors = [numtovect(k,N) for k in range(2**(2**N))]\n",
    "for i, v in enumerate(vectors):\n",
    "    print(\"k=\",i,\" \", v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "c_cir(N,inputVector, weightVector)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# Tempi in funzione di N (sequenziale)\n",
    "# N - Tempo totale - Tempo medio per vettore d'input (riga)\n",
    "# 2 - 0:00:15.781950 - 0:00:01.236381 * 16\n",
    "# 3 - 4:24:20.956383 - 0:01:16.679671 * 256\n",
    "# 4 - ?:??:??.?????? -  * 65536\n",
    "\n",
    "\n",
    "# Tempi con il transpiling di Qiskit\n",
    "# N - tot\n",
    "# 2 - 0:00:06.797629\n",
    "# 3 - 0:04:56.086619"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Add to your import statements\n",
    "from qiskit.compiler import transpile, assemble\n",
    "\n",
    "# After creating qc_list\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "transpiled_circs = transpile(qc_list, backend=backend)\n",
    "qobjs = assemble(transpiled_circs, backend=backend)\n",
    "job_info = backend.run(qobjs)\n",
    "\n",
    "# To get the results\n",
    "for circ_index in range(len(transpiled_circs)):\n",
    "    print(job_info.result().get_counts(transpiled_circs[circ_index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "jupyter": {
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    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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    "pycharm": {
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    }
   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
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