docs/tutorials/notebooks/tutorial_quantum_gates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial Quantum Gates\n",
    "\n",
    "This notebook aims to give a clear and quick tutorial to use and exploit this library to simulate the noise inside quantum device for the release 2.1.0.\n",
    "\n",
    "We assume you follow the procedure to install the library. You can find the complete documentation [here](https://quantum-gates.readthedocs.io/en/latest/index.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the simulation for linear topology\n",
    "\n",
    "First of all import the necessary packages to run a simulation. Also import your IBM TOKEN if you want to simulate the behaviour of a real device, otherwise you can use the Fake device of IBM without a TOKEN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard libraries\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "# Qiskit\n",
    "from qiskit import QuantumCircuit, transpile\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_ibm_runtime.fake_provider import FakeKyiv\n",
    "\n",
    "# Own library\n",
    "from quantum_gates.simulators import MrAndersonSimulator\n",
    "from quantum_gates.gates import standard_gates\n",
    "from quantum_gates.circuits import EfficientCircuit, BinaryCircuit\n",
    "from quantum_gates.utilities import DeviceParameters\n",
    "from quantum_gates.utilities import setup_backend\n",
    "from quantum_gates.utilities import fix_counts\n",
    "IBM_TOKEN = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create a quantum circuit with Qiskit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 538.128x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_qubit = 2\n",
    "n_classic_bit = 2\n",
    "\n",
    "circ = QuantumCircuit(n_qubit,n_classic_bit)\n",
    "circ.h(0)\n",
    "circ.cx(0,1)\n",
    "circ.barrier(range(n_qubit))\n",
    "circ.measure(range(n_qubit),range(n_classic_bit))\n",
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the configuration from a JSON file or directly from the code, as demonstrated in the quickstart section of the README.md. Here, we define this information in a slightly different but equivalent way to what's done in the quickstart.\n",
    "\n",
    "You can choose to use qiskit functions, otherwise you can use our function ```setup_backend``` to setup the backend as in the quick start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# directly with qiskit import a real device\n",
    "provider = QiskitRuntimeService(channel= 'ibm_quantum', token=IBM_TOKEN)\n",
    "provider.backends() \n",
    "backend_real = provider.backend('ibm_kyiv')\n",
    "\n",
    "# fake backend without the use of the IBM_TOKEN\n",
    "backend_fake = FakeKyiv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the backend we have to define the information for the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_qubit_layout = [0,1] # linear layout\n",
    "psi0 = [1] + [0] * (2**n_qubit-1) # starting state\n",
    "shots = 100 # shots\n",
    "device_param = DeviceParameters(linear_qubit_layout)\n",
    "device_param.load_from_backend(backend_fake)\n",
    "device_param_lookup = device_param.__dict__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then transpile the Qiskit circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transpilation of the circuit\n",
    "t_circ = transpile(\n",
    "    circ,\n",
    "    backend_fake,\n",
    "    initial_layout=linear_qubit_layout,\n",
    "    seed_transpiler=10,\n",
    "    scheduling_method='asap'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 841.441x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_circ.draw('mpl', idle_wires= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now define the simulator through the ```MrAndersonSimulator``` class and perform the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = MrAndersonSimulator(gates=standard_gates, CircuitClass=EfficientCircuit, parallel= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our CPU count is 8\n",
      "Use 80% of the cores, so 6 processes.\n",
      "As we perform 100 shots, we use a chunksize of 17.\n"
     ]
    }
   ],
   "source": [
    "probs = sim.run(\n",
    "    t_qiskit_circ=t_circ, \n",
    "    qubits_layout=linear_qubit_layout, \n",
    "    psi0=np.array(psi0), \n",
    "    shots=shots, \n",
    "    device_param=device_param_lookup,\n",
    "    nqubit=n_qubit) # here level opt is meaningless becaues we are using efficient circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can switch form Big-Endian order to Little-Endian order of the output to match Qiskit standard through the function ```fix_counts``` and plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measured_qubit = n_classic_bit\n",
    "counts_ng = fix_counts(probs, n_classic_bit)\n",
    "plot_histogram(counts_ng, bar_labels=False, legend=['Noisy Gates simulation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non linear topology\n",
    "\n",
    "Now we implement the same experiment but considering non linear topology. \n",
    "You can find documentation on the IBM processor types [here](https://docs.quantum.ibm.com/guides/processor-types) to see the hardware topologies. \n",
    "\n",
    "We are using the `ibm_kyiv` device which use a Eagle r3 processor.\n",
    "\n",
    "First of all define a new qubit layout and consequentially import again the device parameter.\n",
    "\n",
    "**Note:** If you are using a non linear topology you must import the information of all qubits up to the one with the max index used, otherwise the program doesn't work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_linear_qubit_layout = [0,14] # linear layout\n",
    "psi0 = [1] + [0] * (2**n_qubit-1) # starting state\n",
    "shots = 100 # shots\n",
    "device_param = DeviceParameters(list(np.arange(max(non_linear_qubit_layout)+1)))\n",
    "device_param.load_from_backend(backend_fake)\n",
    "device_param_lookup = device_param.__dict__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is similat as before "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transpilation of the circuit\n",
    "t_circ_2 = transpile(\n",
    "    circ,\n",
    "    backend_fake,\n",
    "    initial_layout=non_linear_qubit_layout,\n",
    "    seed_transpiler=10,\n",
    "    scheduling_method='asap'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 859.874x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_circ_2.draw('mpl', idle_wires= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a new simulator object using the `BinaryCircuit` class that allows to run experiment with non-linear topologies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_2 = MrAndersonSimulator(gates=standard_gates, CircuitClass=BinaryCircuit, parallel= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and run the simulation as before taking care to modify the input parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our CPU count is 8\n",
      "Use 80% of the cores, so 6 processes.\n",
      "As we perform 100 shots, we use a chunksize of 17.\n"
     ]
    }
   ],
   "source": [
    "probs_2 = sim_2.run(\n",
    "    t_qiskit_circ=t_circ_2, \n",
    "    qubits_layout=non_linear_qubit_layout, \n",
    "    psi0=np.array(psi0), \n",
    "    shots=shots, \n",
    "    device_param=device_param_lookup,\n",
    "    nqubit=n_qubit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measured_qubit = n_classic_bit\n",
    "counts_ng = fix_counts(probs, measured_qubit)\n",
    "plot_histogram(counts_ng, bar_labels=False, legend=['Noisy Gates simulation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison with Qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make a comparison with Qiskit to see the differences in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simulation with Qiskit\n",
    "t_circ = transpile(\n",
    "    circ,\n",
    "    backend_fake,\n",
    "    initial_layout=non_linear_qubit_layout,\n",
    "    seed_transpiler=42,\n",
    ")\n",
    "\n",
    "# Run the transpiled circuit using the fake backend\n",
    "job = backend_fake.run(t_circ, shots=1000)\n",
    "counts_qiskit = job.result().get_counts()\n",
    "plot_histogram(counts_qiskit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure less qubits respect the ones used\n",
    "\n",
    "The simulator allows you also to measure less qubits that the ones you use in the computation just re-defining the measurament in the Qiskit circuit. You can do it  independently from the topology of the layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 454.517x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_qubit = 2\n",
    "\n",
    "circ = QuantumCircuit(n_qubit,n_classic_bit)\n",
    "circ.h(0)\n",
    "circ.cx(0,1)\n",
    "circ.barrier(range(n_qubit))\n",
    "circ.measure(qubit = 0, cbit = 0)\n",
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat again all the instruction to run the simulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_qubit_layout = [0,1] # linear layout\n",
    "psi0 = [1] + [0] * (2**n_qubit-1) # starting state\n",
    "shots = 100 # shots\n",
    "device_param = DeviceParameters(linear_qubit_layout)\n",
    "device_param.load_from_backend(backend_fake)\n",
    "device_param_lookup = device_param.__dict__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transpilation of the circuit\n",
    "t_circ = transpile(\n",
    "    circ,\n",
    "    backend_fake,\n",
    "    initial_layout=linear_qubit_layout,\n",
    "    seed_transpiler=10,\n",
    "    scheduling_method='asap'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 841.441x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_circ.draw('mpl', idle_wires= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = MrAndersonSimulator(gates=standard_gates, CircuitClass=EfficientCircuit, parallel= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our CPU count is 8\n",
      "Use 80% of the cores, so 6 processes.\n",
      "As we perform 100 shots, we use a chunksize of 17.\n"
     ]
    }
   ],
   "source": [
    "probs = sim.run(\n",
    "    t_qiskit_circ=t_circ, \n",
    "    qubits_layout=linear_qubit_layout, \n",
    "    psi0=np.array(psi0), \n",
    "    shots=shots, \n",
    "    device_param=device_param_lookup,\n",
    "    nqubit=n_qubit) # here level opt is meaningless becaues we are using efficient circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here take care to modify the number of measured qubit for the switching from Big to Little Endian convetion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measured_qubit = 1\n",
    "counts_ng = fix_counts(probs, measured_qubit)\n",
    "plot_histogram(counts_ng, bar_labels=False, legend=['Noisy Gates simulation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we obtain the result when measuring only one of the qubits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that all the functionalities are displayed you can use and modify this notebook to run more complex simulation using the simulator. \n",
    "\n",
    "Enjoy it!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Quantum1",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}