qiskit-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐     ┌─┐   \n",
       "q_0: |0>┤ X ├──■──┤M├───\n",
       "        └───┘┌─┴─┐└╥┘┌─┐\n",
       "q_1: |0>─────┤ X ├─╫─┤M├\n",
       "             └───┘ ║ └╥┘\n",
       " c_0: 0 ═══════════╩══╬═\n",
       "                      ║ \n",
       " c_1: 0 ══════════════╩═\n",
       "                        </pre>"
      ],
      "text/plain": [
       "        ┌───┐     ┌─┐   \n",
       "q_0: |0>┤ X ├──■──┤M├───\n",
       "        └───┘┌─┴─┐└╥┘┌─┐\n",
       "q_1: |0>─────┤ X ├─╫─┤M├\n",
       "             └───┘ ║ └╥┘\n",
       " c_0: 0 ═══════════╩══╬═\n",
       "                      ║ \n",
       " c_1: 0 ══════════════╩═\n",
       "                        "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import qiskit as q\n",
    "%matplotlib inline\n",
    "\n",
    "circuit = q.QuantumCircuit(2, 2) # 2 qubits, 2 classical bits\n",
    "\n",
    "# currently: 0, 0\n",
    "circuit.x(0) # not gate applied to qubit 0\n",
    "# 1, 0\n",
    "circuit.cx(0, 1) # cnot, controlled not. Entangles i.e. Flips 2nd qubit value IF first qubit is a 1\n",
    "# 1, 1\n",
    "circuit.measure([0, 1], [0, 1]) # Looking in Schrodinger's box(Value collapses). Direct repr. of qubits to classical bits\n",
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 418.992x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "configrc.store_credentials:WARNING:2020-04-08 07:21:10,843: Credentials already present. Set overwrite=True to overwrite.\n"
     ]
    }
   ],
   "source": [
    "# Save your account\n",
    "from qiskit import IBMQ\n",
    "IBMQ.save_account(open(\"token.txt\",\"r\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibmq_qasm_simulator has 3 queued and simulated qubits\n",
      "ibmqx2 has 5 queued and 5 qubits\n",
      "ibmq_16_melbourne has 17 queued and 15 qubits\n",
      "ibmq_vigo has 4 queued and 5 qubits\n",
      "ibmq_ourense has 2 queued and 5 qubits\n",
      "ibmq_london has 2 queued and 5 qubits\n",
      "ibmq_burlington has 3 queued and 5 qubits\n",
      "ibmq_essex has 3 queued and 5 qubits\n",
      "ibmq_armonk has 3 queued and 1 qubits\n"
     ]
    }
   ],
   "source": [
    "# Check machines for backend and figure out queue and available qubits per machine\n",
    "# Choose simulator for practice and while deploying research work try using machine with lowest queue while aving required no. of qubits\n",
    "provider = IBMQ.get_provider('ibm-q')\n",
    "for backend in provider.backends():\n",
    "    try:\n",
    "        qubit_count = len(backend.properties().qubits)\n",
    "    except:\n",
    "        qubit_count = 'simulated'\n",
    "    print(\"{} has {} queued and {} qubits\".format(backend.name(), backend.status().pending_jobs, qubit_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "backend = provider.get_backend('ibmq_london')\n",
    "job = q.execute(circuit, backend = backend, shots = 500) \n",
    "# We need large no. of shots because in each shot when measured qubits collapse to single value. Large no. of shots to figure out the exp. results\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_histogram\n",
    "from matplotlib import style\n",
    "\n",
    "style.use('dark_background')\n",
    "result = job.result()\n",
    "counts = result.get_counts(circuit)\n",
    "\n",
    "plot_histogram([counts])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 418.992x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run on a simulator(if machine busy or you want a simulation on a perfect quantum comp(noise eliminated))\n",
    "##backend = provider.get_backend('ibmq_qasm_simulator')\n",
    "# Till now we examined the entangled property\n",
    "# Now we will be looking at superposition principle\n",
    "\n",
    "circuit = q.QuantumCircuit(2, 2) # 2 qubits, 2 classical bits\n",
    "\n",
    "# currently: 0, 0\n",
    "circuit.h(0) # hadamard gate applied. Puts the given qubit in superposition\n",
    "# 1, 0\n",
    "circuit.cx(0, 1) # cnot, controlled not. Entangles i.e. Flips 2nd qubit value IF first qubit is a 1\n",
    "# 1, 1\n",
    "circuit.measure([0, 1], [0, 1]) # Looking in Schrodinger's box(Value collapses). Direct repr. of qubits to classical bits\n",
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "job = q.execute(circuit, backend = backend, shots = 500) \n",
    "# We need large no. of shots because in each shot when measured qubits collapse to single value. Large no. of shots to figure out the exp. results\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = job.result()\n",
    "counts = result.get_counts(circuit)\n",
    "\n",
    "plot_histogram([counts])\n",
    "# We had put qubit 1 on superposition and qubit 2 would switch up only when qubit 1 was a 1.\n",
    "# Shows that we have nearly equal probabilities of having 0 and 1.\n",
    "# Some practical deviations due to quantum noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qasm_simulator\n",
      "statevector_simulator\n",
      "unitary_simulator\n",
      "pulse_simulator\n"
     ]
    }
   ],
   "source": [
    "# Running on a local simulator\n",
    "\n",
    "from qiskit import Aer # Simulator framework for qiskit\n",
    "sim_backend = Aer.get_backend('qasm_simulator')\n",
    "# Checking for available backends\n",
    "# qasm simulator resembles actual quantum machine closely\n",
    "for backend in Aer.backends():\n",
    "    print(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "job = q.execute(circuit, backend = sim_backend, shots = 500)\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram([counts])\n",
    "# Here we see \n",
    "# We had put qubit 1 on superposition and qubit 2 would switch up only when qubit 1 was a 1.\n",
    "# Shows that we have nearly equal probabilities of having 0 and 1.\n",
    "# Hence shows superposition principle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}