nb_ex11_3_qai_hybrid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4414fce-984f-47e6-ab00-641edd273244",
   "metadata": {},
   "source": [
    "## 11.3 양자인공지능 알고리즘 구현"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694a52dc-b4fc-4b03-a1c3-267be6a27cee",
   "metadata": {},
   "source": [
    "### 11.3.2 양자인공지능 학습하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14030068-2d7f-4e2c-97d3-3f6a639b07fa",
   "metadata": {},
   "source": [
    "#### 양자인공지능 패키지 설치하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f37f75d-22a6-464d-ac3c-e84e431a5a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install python=3.7\n",
    "!pip install tensorflow==2.4.1\n",
    "!pip install tensorflow-quantum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadda977-7771-4fc5-ad28-5d5d1c647024",
   "metadata": {},
   "source": [
    "#### 관련 패키지 부르기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b79004e-696b-4157-9ada-65ef3a1bc8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_quantum as tfq\n",
    "import cirq\n",
    "from cirq.contrib.svg import SVGCircuit\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import Sequential, Input, Model\n",
    "\n",
    "import sympy\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f685de4-e2fc-4686-b5ca-132ddab95a68",
   "metadata": {},
   "source": [
    "#### 입력 데이터와 기대 출력 레이블 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70132063-2eea-4d05-a495-b65aa660f14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Classic = np.array([[0], [1]], dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ffd112c-df70-4d43-a907-cf3aa506c93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Qubit = cirq.GridQubit(0, 0)\n",
    "\n",
    "Initial_rotation_phase = np.random.uniform(0, 2 * np.pi)\n",
    "X_Quantum_circuit = cirq.Circuit(\n",
    "  cirq.ry(Initial_rotation_phase)(Qubit)\n",
    ")\n",
    "X_Quantum = tfq.convert_to_tensor(\n",
    "  [X_Quantum_circuit]*len(X_Classic)) \n",
    "\n",
    "Y = np.array([[1], [-1]], dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ae0d28-0ce5-4eed-9858-b7493d433b70",
   "metadata": {},
   "source": [
    "#### 전통인공지능 부분 만들기: 파라미터 제어 뉴럴넷"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "621632c5-1df2-4654-b030-8afd5bcc48e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_classical_NN(x):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(2, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "    return model(x)\n",
    "\n",
    "Classical_NN_In = Input(shape=(1,), dtype=tf.dtypes.float32, \n",
    "                         name='commands_input')\n",
    "Classical_NN = make_classical_NN(Classical_NN_In)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6694b499-bdd9-47b2-9c59-cb26704efede",
   "metadata": {},
   "source": [
    "#### 파라미터를 포함하는 양자회로 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ce8f4d6-97a7-4be1-8885-df8c952365a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"178.9504296875\" height=\"50.0\"><line x1=\"34.7588671875\" x2=\"148.9504296875\" y1=\"25.0\" y2=\"25.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><rect x=\"10.0\" y=\"5.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(0, 0): </text><rect x=\"79.517734375\" y=\"5.0\" width=\"49.4326953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"104.23408203125001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(W)</text></svg>",
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f5dbc310790>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = sympy.symbols('W')\n",
    "Quantum_circuit = cirq.Circuit(cirq.ry(W)(Qubit))\n",
    "SVGCircuit(Quantum_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e45c05-c2e0-4bc2-bf7e-645467cf0a0c",
   "metadata": {},
   "source": [
    "### 하이브리드 인공지능 만들기: 양자 및 전통 인공지능 복합형 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7eed31f-6820-4a3e-bb3f-f2b9f9de5517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quantum_In = Input(shape=(),dtype=tf.string, name='circuits_input')\n",
    "Quantum_layer = tfq.layers.ControlledPQC(Quantum_circuit,\n",
    "                                          operators = cirq.Z(Qubit))\n",
    "expectation = Quantum_layer([Quantum_In, Classical_NN])\n",
    "model = Model(inputs=[Quantum_In, Classical_NN_In],\n",
    "                       outputs=expectation)\n",
    "tf.keras.utils.plot_model(model, show_shapes=True, dpi=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "882e83aa-3f0d-4757-a6a9-e6b53bffb640",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "loss = tf.keras.losses.MeanSquaredError()\n",
    "model.compile(optimizer=optimizer, loss=loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b58c577d-b3df-42a3-a953-aba31ccc75d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(x=[X_Quantum, X_Classic],\n",
    "                    y=Y, epochs=50, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "656b271a-a3d5-4d84-9754-ae1e32bc4954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ranbnjO3NoPxsbnllNjvKdbN7EWl5LFFdFmZ2GjDR3SeF4+cD49z9qmrL5AAvAkOAHOBMd3+llte6FLgUID8/f/RTTz3VoJqKi4vJzs5u0LqNMXtDJb+fsoNTBsb5Qf9oNniianvU1O7konbv2oQJE6a5+5ja5qUmpKqA1TKtZuocDXwOHAH0B94ws/fc/Vt7V939fuB+gDFjxnhBQUGDCiosLKSh6zZGATBzxzQmz13LL04dR48O7Zq9hqjaHjW1O7mo3Q2TyK6hlUCvauM9ga9rLHMx8LwHFgJLCLYO2pwbjwt2j9z6iq5DJCItSyKDYAow0Mz6hjuAzyLoBqpuOfA9ADPLBwYDixNYU2R6dGjHVRMGMHnGat5foOsQiUjLkbAgcPcK4CrgNWAO8Iy7zzKzy83s8nCx/wQONrMZwFvAte7eZj8lJ43vx94dM7npxZmUVeg6RCLSMiRyHwHuPhmYXGPavdWGvwaOSmQNLUlGPMZNJwzjh49M5dEPl/Kjw/pFXZKIiM4sbm5HDMnniCFduOPN+azduiPqckREFARR+NXxwyivdH7zj7lRlyIioiCIQp9OWVx6WD9e+OwrPl2yMepyRCTJKQgi8uMJ/emem8Gv/j5TN7ARkUgpCCKSmZbKvx0/jLmri3jyE93ARkSioyCI0DEjunLogE784fV5rC3SjmMRiYaCIEJmxs0nDqe0vIrfTNaOYxGJhoIgYv06Z3PZ4cGO448WbYi6HBFJQgqCFuDKCQPoldeOX/1dZxyLSPNTELQAGfEYvz5hOAvWFvPQB0uiLkdEkoyCoIX43tB8jhqWzx/fXMBXm7dHXY6IJBEFQQvyqxOCS1Xf/NKsiCsRkWSiIGhBeu6Vyb98bwCvzVrD23PXRl2OiCQJBUELM+nQfvTvnMVNL87SPY5FpFkoCFqYtNQU/vOkESzfWMI9by+MuhwRSQIKghbo4P6dOHlUD+59ZzEL1xZHXY6ItHEKghbqxuOG0i4txg0vzMDdoy5HRNowBUEL1Sk7nRuOHcKnSzby7LSVUZcjIm2YgqAFO310L8b2yeO/Js9hQ3Fp1OWISBulIGjBUlKM/zplBNtKK7j1lTlRlyMibZSCoIUb0CWHKw7vz/OffcUHC9dHXY6ItEEKglbgxxMG0KdjJje+MEPnFohIk1MQtAIZ8Ri3nrwPSzeUcLfOLRCRJqYgaCUOGdCJU0b14N53FrFgTVHU5YhIG6IgaEVuPG4oWemp3PDCDKqqdG6BiDQNBUEr0jE7nRuPHcqUpZt48lPd8F5EmoaCoJU5bXRPxg/sxG8nz9F9C0SkSSQ0CMxsopnNM7OFZnbdLpYpMLPPzWyWmb2TyHraAjPjv07ehyqHG3X5CRFpAgkLAjOLAXcDxwDDgLPNbFiNZToA9wA/cPfhwOmJqqct6ZWXyS+PHkzhvHX8/fOvoy5HRFq5OgWBmWWZWUo4PMjMfmBm8T2sNhZY6O6L3b0MeAo4scYy5wDPu/tyAHfX3Vjq6MKD+zCqdwf+46VZrNflJ0SkEawuXQtmNg0YD+wFfAxMBUrc/dzdrHMaMNHdJ4Xj5wPj3P2qasvcAcSB4UAO8Ed3f6yW17oUuBQgPz9/9FNPPVXX9n1LcXEx2dnZDVq3JfqquIqbPtjO6PwYV+yXsdtl21rb60rtTi5q965NmDBhmruPqW1eah3fx9y9xMwuAf7k7r83s8/2tE4t02qmTiowGvge0A74yMw+dvf531rJ/X7gfoAxY8Z4QUFBHcv+tsLCQhq6bku1od0Cbn9jPj/qMpQjh+Xvcrm22Pa6ULuTi9rdMHXdR2BmdhBwLvBKOG1PIbIS6FVtvCdQs0N7JfCqu29z9/XAu8DIOtYkwOWH92dI1xz+7W8z2bqjPOpyRKQVqmsQXA1cD7zg7rPMrB/w9h7WmQIMNLO+ZpYGnAW8WGOZvwPjzSzVzDKBcYAus1kPaakp/O7UfVlbtIPf/mNu1OWISCtUp64hd38HeAcg3Gm83t1/sod1KszsKuA1IAY8FIbI5eH8e919jpm9CnwJVAEPuvvMhjcnOY3s1YFJ4/tx/7uLOXZENw4d2CnqkkSkFanrUUN/MbP2ZpYFzAbmmdkv97Seu09290Hu3t/dbw2n3evu91Zb5r/dfZi7j3D3OxrYjqT38+8Pol/nLK597kuK1EUkIvVQ166hYe6+FTgJmAz0Bs5PVFFSfxnxGLedPpJVW7Zzy8vqXRORuqtrEMTD8wZOAv7u7uV89wggidio3ntx2eH9eXrqCt6eq1MyRKRu6hoE9wFLgSzgXTPbG9iaqKKk4a4+ciCD83O47vkv2VKiLiIR2bM6BYG73+nuPdz9WA8sAyYkuDZpgPTUGLedMZINxWX8+qVZUZcjIq1AXXcW55rZ7WY2NXzcRrB1IC3QiB65XHXEAF747Ctenbk66nJEpIWra9fQQ0ARcEb42Ao8nKiipPGunDCAET3ac+MLM9igaxGJyG7UNQj6u/tN4QXkFrv7fwD9ElmYNE48lsJtp+9H0Y4K/v3vM3W5ahHZpboGwXYzO3TniJkdAuiuKC3c4K45XP39gUyesZqPVlVGXY6ItFB1vejc5cBjZpYbjm8CLkxMSdKULjusP2/PXcvjszdx/sYSeuVlRl2SiLQwdT1q6At3HwnsC+zr7qOAIxJamTSJWIpx+xn7AfCzpz+norIq2oJEpMWp1x3K3H1reIYxwM8TUI8kQK+8TC4Yls7UZZu4p3BR1OWISAvTmFtV1na/AWmhDuqeykn7deePby1g2rJNUZcjIi1IY4JAh6G0MjefNIJuuRlc/fRnujCdiHxjt0FgZkVmtrWWRxHQvZlqlCbSPiPOHWfux1ebtvPrF2dHXY6ItBC7DQJ3z3H39rU8cty9rkccSQsypk8eVx0xkOemr+SlL2reME5EklFjuoaklfrJEQMY1bsDN7wwg5WbSqIuR0QipiBIQqmxFP545ijc4Sd//YxyHVIqktQUBEmqd8dMfnPKPkxfvpnfv6p7HYskMwVBEjthZHcuOGhvHnhvCa/P0lVKRZKVgiDJ3XjcUPbpkcs1z37Bio3aXyCSjBQESS49NcY95+6PA1f+ZTqlFbo4nUiyURAIvfIy+cPpI/ly5RZ+M1n7C0SSjYJAADh6eFcmHdqXRz5cyitfroq6HBFpRgoC+ca1xwxh/94duPa5L1myflvU5YhIM1EQyDfisRTuOmd/UmPGFU9MY1tpRdQliUgzUBDIt3Tv0I4/nT2K+WuKuObZL3SLS5EkkNAgMLOJZjbPzBaa2XW7We4AM6s0s9MSWY/UzfiBnbnh2KH8Y+Zq7vrnwqjLEZEES1gQmFkMuBs4BhgGnG1mw3ax3O+A1xJVi9TfJYf25eRRPbjtjfm8OXtN1OWISAIlcotgLLDQ3Re7exnwFHBiLcv9C/AcsDaBtUg9mRm/OWUf9u2Zy9VPf87CtUVRlyQiCZLIIOgBrKg2vjKc9g0z6wGcDNybwDqkgTLiMe49bzQZ8RR+9Ng0tmzXzWxE2qJE3lOgtltZ1tzzeAdwrbtXmu36zpdmdilwKUB+fj6FhYUNKqi4uLjB67Z2jWn7pcNT+N2n2zjv7rf42eh0Unbzu2ppkvV3rnYnl8a2O5FBsBLoVW28J1DzTihjgKfCEOgEHGtmFe7+t+oLufv9wP0AY8aM8YKCggYVVFhYSEPXbe0a0/YCILv7cm54YQaf7OjK9ccMbcrSEipZf+dqd3JpbLsTGQRTgIFm1hf4CjgLOKf6Au7ed+ewmT0CvFwzBKRlOGdcb2av2sJ97yymT8cszh7bO+qSRKSJJCwI3L3CzK4iOBooBjzk7rPM7PJwvvYLtDI3nTCcFRu3829/m0m33AwKBneJuiQRaQIJPY/A3Se7+yB37+/ut4bT7q0tBNz9Inf/f4msRxonHkvh7nP3Z3B+Dlc+OZ1ZX2+JuiQRaQI6s1jqJTs9lYcvPoDcdnF++MgUvt68PeqSRKSRFARSb/ntM3j44rGUlFZy8cNT2LpDh5WKtGYKAmmQwV1zuPf80SxaV8wVT0yjrKIq6pJEpIEUBNJghwzoxG9P3ZcPFm7g+udn6AJ1Iq1UIg8flSRw2uiefLVpO//z5nzysuLccOxQdndyoIi0PAoCabSffG8Am0rKeOC9JWSnx/npkQOjLklE6kFBII1mZvzq+GEUl1bwP2/OJys9xqTx/aIuS0TqSEEgTSIlxfjtKftQUlbBLa/MITs9lbN09rFIq6AgkCaTGkvhjjNHsa10Kte/MIOs9FROGNk96rJEZA901JA0qbTUFO49bzQH9MnjZ09/zltzdFMbkZZOQSBNrl1ajP+9cAzDurfniien896CdVGXJCK7oSCQhMjJiPPoxWPp3zmbSx6dyj/nastApKVSEEjC7JWVxl9/NI7B+Tlc9vg0Xp25OuqSRKQWCgJJqA6ZaTwxaRwjeuRy5V+m89IXNe9NJCJRUxBIwuW2i/P4JeMY3XsvfvrUZzw/fWXUJYlINQoCaRbZ6ak88sMDOLBfR37x7Bc8PWV51CWJSEhBIM0mMy2Vhy46gMMGduba52bw0PtLoi5JRFAQSDPLiMe4/4LRHD08n5tfns1vJs+hqkpXLRWJkoJAml16aox7zh3N+QfuzX3vLubqpz+ntKIy6rJEkpYuMSGRiKUYN584nG4dMvj9q/NYV1TKfReMpn1GPOrSRJKOtggkMmbGjwsGcPsZI5mydCNn3PsRq7fsiLoskaSjIJDInbJ/Tx6++ABWbtrOyfd8wLzVRVGXJJJUFATSIowf2JmnLzuQiirn1D9/yBuzdUkKkeaiIJAWY3j3XP5+5SH07ZTFjx6byp1vLdARRSLNQEEgLUr3Du149vKDOHlUD25/Yz4/fnI6xaUVUZcl0qYpCKTFyYjHuP2MkfzbcUN5ffZqTrnnA5Zt2BZ1WSJtloJAWiQzY9L4fjz2w3GsLSrlB3d9wLvzdV8DkURIaBCY2UQzm2dmC83sulrmn2tmX4aPD81sZCLrkdbn0IGdePHKQ+mWm8GFD3/KH16bR0VlVdRlibQpCQsCM4sBdwPHAMOAs81sWI3FlgCHu/u+wH8C9yeqHmm9enfM5PkfH8zpo3ty19sLOfP+j1m5qSTqskTajERuEYwFFrr7YncvA54CTqy+gLt/6O6bwtGPgZ4JrEdascy0VH5/2kj+eNZ+zFtdxLF/fI9/zFgVdVkibYK5J+bwPDM7DZjo7pPC8fOBce5+1S6WvwYYsnP5GvMuBS4FyM/PH/3UU081qKbi4mKys7MbtG5r15bavrakinu/KGXxlioKeqVyzpA00mJW67Jtqd31oXYnl7q0e8KECdPcfUxt8xJ5raHa/jNrTR0zmwBcAhxa23x3v5+w22jMmDFeUFDQoIIKCwtp6LqtXVtr+0lHVXHbG/O4753FfF2WwW2n78c+PXO/s1xba3ddqd3JpbHtTmTX0EqgV7XxnsB37lNoZvsCDwInuvuGBNYjbUhaagrXHzOUx344ls0l5Zx0zwf8/tW57CjXVUxF6iuRQTAFGGhmfc0sDTgLeLH6AmbWG3geON/d5yewFmmjDhvUmTd+djinjOrBPYWLOP5P7zN9+aY9rygi30hYELh7BXAV8BowB3jG3WeZ2eVmdnm42K+AjsA9Zva5mU1NVD3SduVmxvnv00fy6A/HUlJawal//pBbXp7N9jJtHYjURULvR+Duk4HJNabdW214EvCdncMiDXH4oM689rPD+N2rc3nw/SW8OWcNp/WtoiDqwkRaOJ1ZLG1KTkacW07ah7/+6EAA/jB1B5c9PpUVG3XegciuKAikTTqof0de+9lhnDYwzrvz13Pk7e9w+xvz1V0kUgsFgbRZ6akxju+fxj+vOZyjh3flzrcWcOTt7zB5xioSdf6MSGukIJA2r1tuO+48exTPXHYQ7dvF+fGT0znjvo+YsnRj1KWJtAgKAkkaY/vm8dJVh3DLSSNYtqGE0+/9iIse/pSZX22JujSRSCkIJKmkxlI478C9eeeXE7j+mCF8vmIzx//pfa58cjqL1hVHXZ5IJBJ6+KhIS9UuLcZlh/fn7HG9efDdxTz4/hL+MXMVJ4/qyeWH92Ngfk7UJYo0GwWBJLX2GXF+ftRgLji4D38uXMSTnyzjuekrOXJoFy4/vD9j+uRFXaJIwqlrSATolJ3Ovx8/jA+v+x5XHzmQacs2cdq9H3Hanz/kzdlrqKrSUUbSdmmLQKSavKw0rj5yEJce1o9npqzggfeWMOmxqQzoks1543pzyuietM+IR12mSJPSFoFILTLTUrnokL4U/rKAO87cj6y0GL9+aTbjbn2L65//UkcaSZuiLQKR3YjHUjhpVA9OGtWDGSu38MTHy3jhs6/466cr2K9XB847cG+OGdGVrHT9K0nrpS0CkTrap2cuvzttXz654UhuOmEYRTvKuebZLzjg1jf52dOf8878dVRUVkVdpki96WuMSD3ltotz8SF9uejgPkxdtokXPvuKV75cxQuffUWn7HR+MLI7J43qzj49cjGr/RaaIi2JgkCkgcyMA/rkcUCfPG46YRhvz13H3z77iic+XsZDHyyhd14mRw3L5+gRXdm/917EUhQK0jIpCESaQHpqjIkjujJxRFe2lJTzj5mreHXWah77aBkPvr+ETtlpHDk0n6OHd+XgAR1JT41FXbLINxQEIk0sNzPOWWN7c9bY3hTtKKdw3jpem7Wal79cxVNTVpART+HAfh0ZP7Azhw3sxIAu2epCkkgpCEQSKCcjzgkju3PCyO6UVlTy4aINvDNvHe8uWMd/vjwbgG65GYwf2IlDBnRiXN+OdM3NiLhqSTYKApFmkp4aY8LgLkwY3AWAFRtLeH/het5bsI5XZ67mmakrAeiV146xfToytu9ejO3bkT4dM7XFIAmlIBCJSK+8TM4e25uzx/amorKKOauK+GTJBqYs3cjb89by3PQgGDplpzGyZwf27dmBfXvlMrJnB/Ky0iKuXtoSBYFIC5AaS2Gfnrns0zOXSeP74e4sWlfMp0s2MXXZRr5cuYV/zlvLzhur9cprx749OzCsW3uGdsthSNf2dMvN0JaDNIiCQKQFMjMGdMlhQJcczhnXG4CiHeXM/GorX6zczJcrN/PFis288uWqb9Zpn5HKkG7tGdatPVWby0lftIH+XbLonJ2ugJDdUhCItBI5GXEO6t+Rg/p3/Gba1h3lzF9dxJzVRcxdtZW5q4t4duoKtpVV8tjsj8P1UunXOZv+nbPo1ymLXnmZ9M7LZO+OWeyVGVdIiIJApDVrnxFnTJ+8b903wd154bW36dJ/XxatK/7m8cHC9Tw//atvrZ+dnkrvvEx65bWje4d2dM8Nnztk0L1DOzpnp5OiE+HaPAWBSBtjZuRlpHDowE4cOrDTt+ZtL6tkxaYSlm8oYdnGElZsLGH5xhIWr9vG+wvWs62s8lvLp6YYXXLS6dw+g/ycdLq0Tyc/J4P89hl0ykmjY1Y6HbPT6JSdTkZcJ8m1VgoCkSTSLi3GoPwcBtVyK053Z+uOCr7evD14bNnB15u3s3ZrKWuLdrB0wzY+XbqRzSXltb52VlqMjtnp7JWVxl6ZcfbKTKND+LxXZpzczDRy28Vpn5FK+3bxcDhOWqqufRk1BYGIAMGWRG74AT20W/tdLrejvJJ1RaWsLy5lQ3EZG7aVsr64jA3FZawvLmVTSTC8cG0xm0vKKS6t2O37ZsRTyMmIk5OeSnZGKjkZqWSnp5KdHic7PUZmeipZaTEy04LpmekxMtNiZMSDaZlpMdrFY7RLi7Gjwqmscl3XqZ4SGgRmNhH4IxADHnT339aYb+H8Y4ES4CJ3n57ImkSkcTLiMXrlZdIrL7NOy5dVVLG5pIwt28vZsr2crTvK2bq9IhgOp20rq6BoRwXFpRUU76hgfVEJxaUVbCuroKS0krL6XN77zcnEY0ZGaoz0eIyMeAoZ8RjpqSnhI0Z6PBhOS42RFksh7Zt5wXBaLIV4agrxWAppMSMeC4bjqSnEU4Lx1HB6aoqF01OIpRjxmJEaTk+NGakpwXAsZsFzSjAtxWgxO+oTFgRmFgPuBr4PrASmmNmL7j672mLHAAPDxzjgz+GziLQRaakpdGmfQZf2Db90RllFFSVlFWwrq2RbaQXbyyopKatkR3nwvL28ku1lFcycu4AevfuwvTyYt6O8KnyupKyiitKKKkorKtm2rYKyimBeeaVTWlFFWUUQOGUVVTTXLapjYTDELAiJlJT/e45ZMC8lhW+C4+yxvZk0vl+T15HILYKxwEJ3XwxgZk8BJwLVg+BE4DF3d+BjM+tgZt3cfdV3X05EklVaagppqWl02MNGSGHpUgoKBjb6/SoqqyivdMoqqyjf+ahwyiqD4KgI51VUVlFRtXPYqawK1quoCsYrqpyKyioqq4Lhbz1XVlHpTmUVVFZVffNcUeVUebBMZRVUebBOVZXTKTu90W2rTSKDoAewotr4Sr77bb+2ZXoA3woCM7sUuBQgPz+fwsLCBhVUXFzc4HVbu2Rtu9qdXKJqtwHx8LHH7R4j6CxvyEFWW7ZSWLjgO5Mb2+5EBkFtnV81N7jqsgzufj9wP8CYMWO8oKCgQQUVFhbS0HVbu2Rtu9qdXNTuhknkcVsrgV7VxnsCXzdgGRERSaBEBsEUYKCZ9TWzNOAs4MUay7wIXGCBA4Et2j8gItK8EtY15O4VZnYV8BpBb9hD7j7LzC4P598LTCY4dHQhweGjFyeqHhERqV1CzyNw98kEH/bVp91bbdiBKxNZg4iI7J7O7RYRSXIKAhGRJKcgEBFJcubeTOdSNxEzWwcsa+DqnYD1TVhOa5KsbVe7k4vavWt7u3vn2ma0uiBoDDOb6u5joq4jCsnadrU7uajdDaOuIRGRJKcgEBFJcskWBPdHXUCEkrXtandyUbsbIKn2EYiIyHcl2xaBiIjUoCAQEUlySRMEZjbRzOaZ2UIzuy7qehLFzB4ys7VmNrPatDwze8PMFoTPe0VZYyKYWS8ze9vM5pjZLDP7aTi9TbfdzDLM7FMz+yJs93+E09t0u3cys5iZfWZmL4fjbb7dZrbUzGaY2edmNjWc1qh2J0UQVLt/8jHAMOBsMxsWbVUJ8wgwsca064C33H0g8FY43tZUAL9w96HAgcCV4e+4rbe9FDjC3UcC+wETw0u6t/V27/RTYE618WRp9wR336/auQONandSBAHV7p/s7mXAzvsntznu/i6wscbkE4FHw+FHgZOas6bm4O6r3H16OFxE8OHQgzbedg8Uh6M775botPF2A5hZT+A44MFqk9t8u3ehUe1OliDY1b2Rk0X+zhv+hM9dIq4nocysDzAK+IQkaHvYPfI5sBZ4w92Tot3AHcC/AlXVpiVDux143cymhfdzh0a2O6H3I2hB6nRvZGn9zCwbeA642t23mtX2q29b3L0S2M/MOgAvmNmIiEtKODM7Hljr7tPMrCDicprbIe7+tZl1Ad4ws7mNfcFk2SJI9nsjrzGzbgDh89qI60kIM4sThMCT7v58ODkp2g7g7puBQoJ9RG293YcAPzCzpQRdvUeY2RO0/Xbj7l+Hz2uBFwi6vhvV7mQJgrrcP7ktexG4MBy+EPh7hLUkhAVf/f8XmOPut1eb1abbbmadwy0BzKwdcCQwlzbebne/3t17unsfgv/nf7r7ebTxdptZlpnl7BwGjgJm0sh2J82ZxWZ2LEGf4s77J98abUWJYWZ/BQoILku7BrgJ+BvwDNAbWA6c7u41dyi3amZ2KPAeMIP/6zO+gWA/QZttu5ntS7BzMEbwxe4Zd7/ZzDrShttdXdg1dI27H9/W221m/Qi2AiDo2v+Lu9/a2HYnTRCIiEjtkqVrSEREdkFBICKS5BQEIiJJTkEgIpLkFAQiIklOQSBSg5lVhld23PlosguXmVmf6leGFWkJkuUSEyL1sd3d94u6CJHmoi0CkToKrwP/u/D6/5+a2YBw+t5m9paZfRk+9w6n55vZC+G9Ar4ws4PDl4qZ2QPh/QNeD88IFomMgkDku9rV6Bo6s9q8re4+FriL4Ex1wuHH3H1f4EngznD6ncA74b0C9gdmhdMHAne7+3BgM3BqQlsjsgc6s1ikBjMrdvfsWqYvJbgJzOLwAner3b2jma0Hurl7eTh9lbt3MrN1QE93L632Gn0ILhU9MBy/Foi7+y3N0DSRWmmLQKR+fBfDu1qmNqXVhivRvjqJmIJApH7OrPb8UTj8IcEVMAHOBd4Ph98CroBvbh7TvrmKFKkPfRMR+a524R2/dnrV3XceQppuZp8QfIk6O5z2E+AhM/slsA64OJz+U+B+M7uE4Jv/FcCqRBcvUl/aRyBSR+E+gjHuvj7qWkSakrqGRESSnLYIRESSnLYIRESSnIJARCTJKQhERJKcgkBEJMkpCEREktz/Bw9y2w4uRVgJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title(\"Training for Quantum-Classic AI\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7133846d-5a04-4dbe-aa90-2fa1d2019b31",
   "metadata": {},
   "source": [
    "- 모델의 예측 결과 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3cc208f5-136c-4564-88c3-35dfbd8a7cd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted_Y = [ 0.90674293 -0.99880725]\n",
      "Y - Predicted_Y = [ 0.09325707 -0.00119275]\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model([X_Quantum, X_Classic]).numpy()\n",
    "Err = Y - Y_pred\n",
    "print('Predicted_Y =', Y_pred.flatten()) \n",
    "print('Y - Predicted_Y =', Err.flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383b2ba2-d30e-46ee-b831-db8f07e62315",
   "metadata": {},
   "source": [
    "---\n",
    "#### 전체 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "712359e6-877f-4367-bdd3-f67cfe470856",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted_Y = [ 0.8931752 -0.9999619]\n",
      "Y - Predicted_Y = [ 1.06824815e-01 -3.80873680e-05]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_quantum as tfq\n",
    "import cirq\n",
    "from cirq.contrib.svg import SVGCircuit\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import Sequential, Input, Model\n",
    "\n",
    "import sympy\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## 입력 데이터와 기대 출력 레이블 만들기\n",
    "X_Classic = np.array([[0], [1]], dtype=np.float32)\n",
    "\n",
    "Qubit = cirq.GridQubit(0, 0)\n",
    "Initial_rotation_phase = np.random.uniform(0, 2 * np.pi)\n",
    "X_Quantum_circuit = cirq.Circuit(\n",
    "  cirq.ry(Initial_rotation_phase)(Qubit)\n",
    ")\n",
    "X_Quantum = tfq.convert_to_tensor(\n",
    "  [X_Quantum_circuit]*len(X_Classic)) \n",
    "\n",
    "Y = np.array([[1], [-1]], dtype=np.float32)\n",
    "\n",
    "## 전통인공지능 부분 만들기: 파라미터 제어 뉴럴넷\n",
    "def make_classical_NN(x):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(2, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "    return model(x)\n",
    "Classical_NN_In = Input(shape=(1,), dtype=tf.dtypes.float32, name='commands_input')\n",
    "Classical_NN = make_classical_NN(Classical_NN_In)\n",
    "\n",
    "## 파라미터를 포함하는 양자회로 만들기\n",
    "W = sympy.symbols('W')\n",
    "\n",
    "Quantum_circuit = cirq.Circuit(cirq.ry(W)(Qubit))\n",
    "SVGCircuit(Quantum_circuit)\n",
    "\n",
    "## 하이브리드 인공지능 부분 만들기: 양자 및 전통 인공지능 복합형 \n",
    "Quantum_In = Input(shape=(),dtype=tf.string, name='circuits_input')\n",
    "Quantum_layer = tfq.layers.ControlledPQC(Quantum_circuit,\n",
    "                                          operators = cirq.Z(Qubit))\n",
    "expectation = Quantum_layer([Quantum_In, Classical_NN])\n",
    "model = Model(inputs=[Quantum_In, Classical_NN_In],\n",
    "                       outputs=expectation)\n",
    "\n",
    "## 모델 학습 및 결과 확인하기\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "loss = tf.keras.losses.MeanSquaredError()\n",
    "model.compile(optimizer=optimizer, loss=loss)\n",
    "history = model.fit(x=[X_Quantum, X_Classic],\n",
    "                    y=Y,\n",
    "                    epochs=50,\n",
    "                    verbose=0)\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title(\"Training for Quantum-Classic AI\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "Y_pred = model([X_Quantum, X_Classic]).numpy()\n",
    "Err = Y - Y_pred\n",
    "print('Predicted_Y =', Y_pred.flatten()) \n",
    "print('Y - Predicted_Y =', Err.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fbceaa3-27c5-48b0-9d40-ee858a0a8f9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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