example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "661cc049-99a3-4ee8-bb09-d32c285f2560",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from qiskit_aer import AerSimulator\n",
    "import matplotlib.pyplot as plt\n",
    "from transcode import create_circuit, measure_cg, decode_cg, decode_au, measure_au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8679b38c-cc27-4fef-918d-34cd3fd12235",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = AerSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "516b75b0-4405-498e-9b10-125f2af3396e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 414.367x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc, qreg, meas  = create_circuit('AUCG')\n",
    "qc.draw('mpl', filename=\"example.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08bf9cf7-40a1-4cef-a5e8-c7b38d728779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_qc = measure_cg(qc, qreg, meas).decompose()\n",
    "counts_cg = sim.run(dec_qc, shots=1000).result().get_counts()\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.bar(x=list(counts_cg.keys()), height=list(counts_cg.values()))\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"counts\")\n",
    "plt.xlabel(\"results\")\n",
    "plt.title(\"z Measurment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6bb464b8-456f-4285-88e4-fdd19611c26c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xxGC'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DNA = decode_cg(counts_cg)\n",
    "DNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ff76913-2a2b-43b1-ac5a-817ade5dcd69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_qc = measure_au(qc, qreg, meas).decompose()\n",
    "counts_au = sim.run(dec_qc, shots=1000).result().get_counts()\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.bar(x=list(counts_au.keys()), height=list(counts_au.values()))\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"counts\")\n",
    "plt.xlabel(\"results\")\n",
    "plt.title(\"x Measurment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d91c053a-8379-4769-8410-73d55ec51f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TAGC'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode_au(counts_au, DNA)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}