bachelor_thesis/computations/Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from itertools import product\n",
    "from collections import defaultdict\n",
    "\n",
    "from pyqcs import CZ, GenericGate, State, H\n",
    "from pyqcs.graph.util import C_L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5000000000000001+0j)*|0b0> + (0.5000000000000001+0j)*|0b1> + (0.5000000000000001+0j)*|0b10> + (0.5000000000000001+0j)*|0b11>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_state_state_2 = (H(0) | H(1)) * State.new_zero_state(2)\n",
    "plus_state_state_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5000000000000001+0j)*|0b0> + (0.5000000000000001+0j)*|0b1> + (0.5000000000000001+0j)*|0b10> + (-0.5000000000000001-0j)*|0b11>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_state_state_2_cz = CZ(0, 1) * plus_state_state_2\n",
    "plus_state_state_2_cz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuits = [GenericGate(0, C_L[c0]) | GenericGate(1, C_L[c1]) for c0,c1 in product(range(24), range(24))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resulting_states = [c * s for c,s in product(circuits, (plus_state_state_2, plus_state_state_2_cz))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_equivalent_states(s, resulting_states):\n",
    "    for i,test in enumerate(resulting_states):\n",
    "        if(test == s):\n",
    "            yield i\n",
    "            \n",
    "equivalent_states = [list(get_equivalent_states(s, resulting_states)) for s in resulting_states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "equivalent_state_counts = [len(e) - 1 for e in equivalent_states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(resulting_states)), equivalent_state_counts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = set((tuple(sorted(v)) for v in equivalent_states))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{15, 23}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(equivalent_state_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1152"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(equivalent_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resulting_states_after_cz = [CZ(0, 1) * s for s in resulting_states]\n",
    "equivalent_states_after_cz = [list(get_equivalent_states(s, resulting_states_after_cz)) for s in resulting_states_after_cz]\n",
    "equivalent_state_counts_after_cz = [len(e) - 1 for e in equivalent_states_after_cz]\n",
    "\n",
    "plt.bar(range(len(resulting_states)), equivalent_state_counts_after_cz)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{15, 23}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_after_cz = set((tuple(sorted(v)) for v in equivalent_states))\n",
    "set(equivalent_state_counts_after_cz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters == clusters_after_cz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "resulting_states_after_cz_C_L = [c*s for c,s in product(circuits, resulting_states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "equivalent_states_after_cz_C_L = [list(get_equivalent_states(s, resulting_states_after_cz_C_L)) for s in resulting_states_after_cz_C_L]\n",
    "equivalent_state_counts_after_cz_C_L = [len(e) - 1 for e in equivalent_states_after_cz_C_L]\n",
    "\n",
    "plt.bar(range(len(resulting_states_after_cz_C_L)), equivalent_state_counts_after_cz_C_L)\n",
    "plt.show()"
   ]
  }
 ],
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