diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..87620ac
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints/
diff --git a/.ipynb_checkpoints/examples-checkpoint.ipynb b/.ipynb_checkpoints/examples-checkpoint.ipynb
deleted file mode 100644
index 91fd780..0000000
--- a/.ipynb_checkpoints/examples-checkpoint.ipynb
+++ /dev/null
@@ -1,251 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "4d3113ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "82f90e0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "cb96a334",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1ae11fc42e0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "b02b160e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#submission part 1\n",
-    "\n",
-    "#load the definition of the encoder and decoder\n",
-    "from part1 import encode,decode\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode(data['image'])\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "    \n",
-    "    #count the number of 2-qubit gates\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #reconstruct the image\n",
-    "    image_re=decode(histogram)\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(data['image'],image_re)\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1 \n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "2fe52c43",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#grade part 2\n",
-    "\n",
-    "#load classifier circuit\n",
-    "with open('part2.pickle', 'rb') as f:\n",
-    "    classifier=pickle.load(f)\n",
-    "    \n",
-    "score=0\n",
-    "\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode(data['image'])\n",
-    "    \n",
-    "    #append with classifier circuit\n",
-    "    \n",
-    "    circuit.append(classifier)\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "    \n",
-    "    #count the gate used in the circuit for score calculation\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #convert histogram to category\n",
-    "    cat=histogram_to_category(histogram)\n",
-    "        \n",
-    "    if cat==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de96fdf1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/part1_example_cirq-checkpoint.ipynb b/.ipynb_checkpoints/part1_example_cirq-checkpoint.ipynb
deleted file mode 100644
index c3d383b..0000000
--- a/.ipynb_checkpoints/part1_example_cirq-checkpoint.ipynb
+++ /dev/null
@@ -1,226 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9c97e0dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7f19ddcc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a443b6a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1f4921cf250>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYCLpP5Ib+DKRvAHvzPWh496ZT3rzvTOSNC6B1bAnZX7mnWkfyPbOdPT7Z8Lp/qtuS1Ikmuad+WxgnHemryjinUHq4A4IAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACRYjxbBKz+3zztSfu3YIJhlcacYZ78wnfRO9Mz3O/69eIguLRpz/oqKSlJ3m/+d0vC/P/zhXnffOIHVwBwQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEi5FiWOXm+C+o+bWMv3lnJqZ3emckqam3yDszoIB3Jpzmvwhn18AY70yignLema6BkHcmN7vHO4PUwR0QAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAEyxGimGVFvRf5HJimv/Cou93T/fOJCqc5r/Aam80wzsTcWnemYzAgHdGktICUf9jBf2PNS6zzzuD1MEdEADABAUEADDhVUC1tbW64YYblJOTo4KCAi1btkyNjY1x+/T09Ki6uloTJkzQuHHjtHz5crW1tSV1aADA6OdVQA0NDaqurtbevXv1zjvvKBKJaNGiReru7o7t89BDD+mtt97SG2+8oYaGBp04cUK333570gcHAIxuXm9C2LFjR9zHmzdvVkFBgQ4cOKD58+ero6NDv/71r7VlyxZ973vfkyRt2rRJ3/jGN7R371595zvfSd7kAIBR7bJeA+ro6JAk5eXlSZIOHDigSCSiysrK2D4zZszQ5MmTtWfPnkE/R29vrzo7O+M2AEDqS7iAotGo1q5dqxtvvFEzZ86UJLW2tiozM1Pjx4+P27ewsFCtra2Dfp7a2lqFw+HYVlpamuhIAIBRJOECqq6u1uHDh/Xqq69e1gA1NTXq6OiIbS0tLZf1+QAAo0NC34i6Zs0avf3229q9e7cmTZoUe7yoqEh9fX1qb2+Puwtqa2tTUVHRoJ8rFAopFAolMgYAYBTzugNyzmnNmjXaunWrdu3apbKysrjn58yZo4yMDO3cuTP2WGNjo44dO6Z58+YlZ2IAQErwugOqrq7Wli1btH37duXk5MRe1wmHw8rKylI4HNZ9992ndevWKS8vT7m5uXrwwQc1b9483gEHAIjjVUAbN26UJC1YsCDu8U2bNmnlypWSpF/+8pcKBoNavny5ent7tXjxYv3qV79KyrAAgNThVUDOffVCkmPGjFFdXZ3q6uoSHgqp63yf/yKcYwL93pkBl9j7azKC/scKBvwXWD03kOmdSWRh0WACi4pK0kACi6UmIpx53jtzdgjmgA3WggMAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmEjoJ6ICierr87/kxgf7hmCSwSWy8nZvAitHZyfwe+px/uculMAK2pIUcWnemZ4EzkNmMLH5kBq4AwIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCxUgxrPrO+S9Y+XF/2DuTk9bjnZGkAQUSyg2HRBYwzRjGxUijbuSeO4xM3AEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwwWKkGFZppzO9MzlB/4VFMwL93hlJCgVcQjlfiSz2maaod6ZjIMs7I0ljgpGEcr7GpvsvsHpmCOaADe6AAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmGAxUgyr9PPDc5yIS+zSzpD/IqY9LsM7Mybgv9jnxPSz3pmeiP9sktQRzfbO9Eb9z3kw4L/AKlIHd0AAABMUEADAhFcB1dbW6oYbblBOTo4KCgq0bNkyNTY2xu2zYMECBQKBuO3+++9P6tAAgNHPq4AaGhpUXV2tvXv36p133lEkEtGiRYvU3d0dt9+qVat08uTJ2LZhw4akDg0AGP28XjXcsWNH3MebN29WQUGBDhw4oPnz58cez87OVlFRUXImBACkpMt6Daijo0OSlJeXF/f4yy+/rPz8fM2cOVM1NTU6d+7cJT9Hb2+vOjs74zYAQOpL+G3Y0WhUa9eu1Y033qiZM2fGHr/77rs1ZcoUlZSU6NChQ3r00UfV2NioN998c9DPU1tbq6eeeirRMQAAo1TCBVRdXa3Dhw/r/fffj3t89erVsV/PmjVLxcXFWrhwoY4ePapp06Zd9Hlqamq0bt262MednZ0qLS1NdCwAwCiRUAGtWbNGb7/9tnbv3q1JkyZ96b4VFRWSpKampkELKBQKKRQKJTIGAGAU8yog55wefPBBbd26VfX19SorK/vKzMGDByVJxcXFCQ0IAEhNXgVUXV2tLVu2aPv27crJyVFra6skKRwOKysrS0ePHtWWLVv0/e9/XxMmTNChQ4f00EMPaf78+Zo9e/aQ/AYAAKOTVwFt3LhR0oVvNv1HmzZt0sqVK5WZmal3331Xzz33nLq7u1VaWqrly5frscceS9rAAIDU4P0luC9TWlqqhoaGyxoIAHBlYDVsDKvcv/pnMuW/YvKUzE/9DyQpN9jjnVmQ5T/f8f4u70xhWpZ35m/Rk94ZSfq4P9M70xKZ4J15+7Ny7wxSB4uRAgBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMMFipBhWE3/f7J05+L/8f0R7XdMC74wkpb+c553Je/+4d8ZlJfBTgNP8/78Y6Oz2P84w6v/PE9YjwBB3QAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwMeLWgnPOSZL6FZGc8TBIvmifd+R8V793ZuBcr3dGkgKRHu9Mf9T/WG7AO6JE/r8YSGC24dTvItYjYAj068Kf6+f/nl9KwH3VHsPs+PHjKi31X3wSADCytLS0aNKkSZd8fsQVUDQa1YkTJ5STk6NAIBD3XGdnp0pLS9XS0qLc3FyjCe1xHi7gPFzAebiA83DBSDgPzjmdPXtWJSUlCgYvfec+4r4EFwwGv7QxJSk3N/eKvsA+x3m4gPNwAefhAs7DBdbnIRwOf+U+vAkBAGCCAgIAmBhVBRQKhbR+/XqFQgn8NMkUwnm4gPNwAefhAs7DBaPpPIy4NyEAAK4Mo+oOCACQOiggAIAJCggAYIICAgCYGDUFVFdXp69//esaM2aMKioq9Kc//cl6pGH35JNPKhAIxG0zZsywHmvI7d69W7feeqtKSkoUCAS0bdu2uOedc3riiSdUXFysrKwsVVZW6siRIzbDDqGvOg8rV6686PpYsmSJzbBDpLa2VjfccINycnJUUFCgZcuWqbGxMW6fnp4eVVdXa8KECRo3bpyWL1+utrY2o4mHxj9zHhYsWHDR9XD//fcbTTy4UVFAr732mtatW6f169frgw8+UHl5uRYvXqxTp05ZjzbsrrvuOp08eTK2vf/++9YjDbnu7m6Vl5errq5u0Oc3bNig559/Xi+++KL27dunsWPHavHixerp8V9YdCT7qvMgSUuWLIm7Pl555ZVhnHDoNTQ0qLq6Wnv37tU777yjSCSiRYsWqbu7O7bPQw89pLfeektvvPGGGhoadOLECd1+++2GUyffP3MeJGnVqlVx18OGDRuMJr4ENwrMnTvXVVdXxz4eGBhwJSUlrra21nCq4bd+/XpXXl5uPYYpSW7r1q2xj6PRqCsqKnLPPPNM7LH29nYXCoXcK6+8YjDh8PjieXDOuRUrVrilS5eazGPl1KlTTpJraGhwzl34s8/IyHBvvPFGbJ+PPvrISXJ79uyxGnPIffE8OOfcd7/7XfejH/3Ibqh/woi/A+rr69OBAwdUWVkZeywYDKqyslJ79uwxnMzGkSNHVFJSoqlTp+qee+7RsWPHrEcy1dzcrNbW1rjrIxwOq6Ki4oq8Purr61VQUKDp06frgQce0JkzZ6xHGlIdHR2SpLy8PEnSgQMHFIlE4q6HGTNmaPLkySl9PXzxPHzu5ZdfVn5+vmbOnKmamhqdO3fOYrxLGnGLkX7R6dOnNTAwoMLCwrjHCwsL9Ze//MVoKhsVFRXavHmzpk+frpMnT+qpp57SzTffrMOHDysnJ8d6PBOtra2SNOj18flzV4olS5bo9ttvV1lZmY4ePaqf/vSnqqqq0p49e5SWlmY9XtJFo1GtXbtWN954o2bOnCnpwvWQmZmp8ePHx+2bytfDYOdBku6++25NmTJFJSUlOnTokB599FE1NjbqzTffNJw23ogvIPxdVVVV7NezZ89WRUWFpkyZotdff1333Xef4WQYCe68887Yr2fNmqXZs2dr2rRpqq+v18KFCw0nGxrV1dU6fPjwFfE66Je51HlYvXp17NezZs1ScXGxFi5cqKNHj2ratGnDPeagRvyX4PLz85WWlnbRu1ja2tpUVFRkNNXIMH78eF177bVqamqyHsXM59cA18fFpk6dqvz8/JS8PtasWaO3335b7733XtyPbykqKlJfX5/a29vj9k/V6+FS52EwFRUVkjSirocRX0CZmZmaM2eOdu7cGXssGo1q586dmjdvnuFk9rq6unT06FEVFxdbj2KmrKxMRUVFcddHZ2en9u3bd8VfH8ePH9eZM2dS6vpwzmnNmjXaunWrdu3apbKysrjn58yZo4yMjLjrobGxUceOHUup6+GrzsNgDh48KEkj63qwfhfEP+PVV191oVDIbd682f35z392q1evduPHj3etra3Wow2rH//4x66+vt41Nze7P/zhD66ystLl5+e7U6dOWY82pM6ePes+/PBD9+GHHzpJ7tlnn3Uffvih++STT5xzzv3iF79w48ePd9u3b3eHDh1yS5cudWVlZe78+fPGkyfXl52Hs2fPuocfftjt2bPHNTc3u3fffdd961vfctdcc43r6emxHj1pHnjgARcOh119fb07efJkbDt37lxsn/vvv99NnjzZ7dq1y+3fv9/NmzfPzZs3z3Dq5Puq89DU1OSefvppt3//ftfc3Oy2b9/upk6d6ubPn288ebxRUUDOOffCCy+4yZMnu8zMTDd37ly3d+9e65GG3R133OGKi4tdZmam+9rXvubuuOMO19TUZD3WkHvvvfecpIu2FStWOOcuvBX78ccfd4WFhS4UCrmFCxe6xsZG26GHwJedh3PnzrlFixa5iRMnuoyMDDdlyhS3atWqlPtP2mC/f0lu06ZNsX3Onz/vfvjDH7qrrrrKZWdnu9tuu82dPHnSbugh8FXn4dixY27+/PkuLy/PhUIhd/XVV7uf/OQnrqOjw3bwL+DHMQAATIz414AAAKmJAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACAif8H8OZooPKJKDYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cb2031cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#submission to part 1, you should make this into a .py file\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "def encode(image):\n",
-    "    circuit=cirq.Circuit()\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))\n",
-    "    return circuit\n",
-    "\n",
-    "def decode(histogram):\n",
-    "    if 1 in histogram.keys():\n",
-    "        image=[[0,0],[0,0]]\n",
-    "    else:\n",
-    "        image=[[1,1],[1,1]]\n",
-    "    return image\n",
-    "\n",
-    "def run_part1(image):\n",
-    "    #encode image into a circuit\n",
-    "    circuit=encode(data['image'])\n",
-    "\n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "\n",
-    "    #reconstruct the image\n",
-    "    image_re=decode(histogram)\n",
-    "\n",
-    "    return circuit,image_re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "385faa44",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit,image_re=run_part1(data['image'])\n",
-    "    \n",
-    "    #count the number of 2qubit gates used\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(data['image'],image_re)\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1 \n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad7e81d7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/part2_example_cirq-checkpoint.ipynb b/.ipynb_checkpoints/part2_example_cirq-checkpoint.ipynb
deleted file mode 100644
index 2bb7a6d..0000000
--- a/.ipynb_checkpoints/part2_example_cirq-checkpoint.ipynb
+++ /dev/null
@@ -1,221 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5519a2aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "11a77286",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dfbb6735",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x18ddbd011c0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "66e9a047",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#you submission\n",
-    "\n",
-    "def encode(image):\n",
-    "    circuit=cirq.Circuit()\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))\n",
-    "    return circuit\n",
-    "\n",
-    "\n",
-    "def run_part2(image):\n",
-    "\n",
-    "    #loade the quantum classifier circuit\n",
-    "    with open('part2.pickle', 'rb') as f:\n",
-    "        classifier=pickle.load(f)\n",
-    "    \n",
-    "    #encode image into circuit\n",
-    "    circuit=encode(image)\n",
-    "    \n",
-    "    #append with classifier circuit\n",
-    "    \n",
-    "    circuit.append(classifier)\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "        \n",
-    "    #convert histogram to category\n",
-    "    label=histogram_to_category(histogram)\n",
-    "        \n",
-    "    return circuit,label\n",
-    "#score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "18e6015f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "score=0\n",
-    "gatecount=0\n",
-    "n=len(dataset)\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #run part 2\n",
-    "    circuit,label=run_part2(data['image'])\n",
-    "    \n",
-    "    #count the gate used in the circuit for score calculation\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #check label\n",
-    "    if label==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a73d2810",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/__pycache__/part1.cpython-39.pyc b/__pycache__/part1.cpython-39.pyc
index 9e215d2..e7f94c0 100644
Binary files a/__pycache__/part1.cpython-39.pyc and b/__pycache__/part1.cpython-39.pyc differ
diff --git a/examples.ipynb b/examples.ipynb
deleted file mode 100644
index 5923d9e..0000000
--- a/examples.ipynb
+++ /dev/null
@@ -1,249 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "4d3113ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "82f90e0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "cb96a334",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1ae11fc42e0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "b02b160e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#grade part1\n",
-    "from part1 import encode_cirq,decode\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode_cirq(data['image'])\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "    \n",
-    "    #count the number of 2-qubit gates\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #reconstruct the image\n",
-    "    image_re=decode(histogram)\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(data['image'],image_re)\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1 \n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "2fe52c43",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#grade part 2\n",
-    "\n",
-    "#load classifier circuit\n",
-    "with open('part2.pickle', 'rb') as f:\n",
-    "    classifier=pickle.load(f)\n",
-    "    \n",
-    "score=0\n",
-    "\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode_cirq(data['image'])\n",
-    "    \n",
-    "    #append with classifier circuit\n",
-    "    \n",
-    "    circuit.append(classifier)\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "    \n",
-    "    #count the gate used in the circuit for score calculation\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #convert histogram to category\n",
-    "    cat=histogram_to_category(histogram)\n",
-    "        \n",
-    "    if cat==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de96fdf1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.0 (main, Oct 25 2022, 14:13:24) [Clang 14.0.0 (clang-1400.0.29.202)]"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples_qiskit.ipynb b/examples_qiskit.ipynb
deleted file mode 100644
index bab8c57..0000000
--- a/examples_qiskit.ipynb
+++ /dev/null
@@ -1,204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qiskit\n",
-    "from qiskit import quantum_info\n",
-    "from qiskit.execute_function import execute\n",
-    "from qiskit import BasicAer\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from typing import Dict, List\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "#this simulate a circuit (without measurement) and output results in the format of histogram.\n",
-    "def simulate(circuit: qiskit.QuantumCircuit) -> dict:\n",
-    "    \"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\n",
-    "    backend = BasicAer.get_backend('statevector_simulator')\n",
-    "    job = execute(circuit, backend)\n",
-    "    result = job.result()\n",
-    "    state_vector = result.get_statevector()\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:\n",
-    "    \"\"\"Returns the number of gate operations with each number of qubits.\"\"\"\n",
-    "    return Counter([len(gate[1]) for gate in circuit.data])\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load data\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#grade part1\n",
-    "from part1 import encode_qiskit,decode\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode_qiskit(data['image'])\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "    \n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #reconstruct \n",
-    "    image_re=decode(histogram)\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(np.asarray(data['image']),np.asarray(image_re))\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1\n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#grade part 2\n",
-    "\n",
-    "#load classifier circuit\n",
-    "classifier=qiskit.QuantumCircuit.from_qasm_file('part2.qasm')\n",
-    "    \n",
-    "score=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit=encode_qiskit(data['image'])\n",
-    "    \n",
-    "    nq1 = circuit.width()\n",
-    "    nq2 = classifier.width()\n",
-    "    nq = max(nq1, nq2)\n",
-    "    qc = qiskit.QuantumCircuit(nq)\n",
-    "    qc.append(circuit.to_instruction(), list(range(nq1)))\n",
-    "    qc.append(classifier.to_instruction(), list(range(nq2)))\n",
-    "\n",
-    "    gatecount+=count_gates(qc)[2]\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(qc)\n",
-    "    \n",
-    "    #convert histogram to category\n",
-    "    cat=histogram_to_category(histogram)\n",
-    "    \n",
-    "    \n",
-    "    if cat==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "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": "3.9.15"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/mock_data_creator.ipynb b/mock_data_creator.ipynb
deleted file mode 100644
index 78ad064..0000000
--- a/mock_data_creator.ipynb
+++ /dev/null
@@ -1,79 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "edbfcaa1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3d1a8015",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#making mock data data1.json and data2.json\n",
-    "\n",
-    "image1={\"image\":[[0,0],[0,0]],\"category\":1}\n",
-    "image2={\"image\":[[1,1],[1,1]],\"category\":0}\n",
-    "\n",
-    "with open('data1.json', \"w\") as outfile:\n",
-    "    json.dump(image1, outfile)\n",
-    "    \n",
-    "with open('data2.json', \"w\") as outfile:\n",
-    "    json.dump(image2, outfile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d36c16ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "circuit=cirq.Circuit()\n",
-    "circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))\n",
-    "#making mock submission for part2\n",
-    "with open('part2.pickle', 'wb') as f:\n",
-    "    pickle.dump(circuit,f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55574177",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/part1.py b/part1.py
deleted file mode 100644
index d133097..0000000
--- a/part1.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import cirq
-import qiskit
-import numpy as np
-
-
-def encode_cirq(image):
-    circuit=cirq.Circuit()
-    if image[0][0]==0:
-        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))
-    return circuit
-
-def encode_qiskit(image):
-    q = qiskit.QuantumRegister(3)
-    circuit = qiskit.QuantumCircuit(q)
-    if image[0][0]==0:
-        circuit.rx(np.pi,0)
-    return circuit
-
-
-def decode(histogram):
-    if 1 in histogram.keys():
-        image=[[0,0],[0,0]]
-    else:
-        image=[[1,1],[1,1]]
-    return image
\ No newline at end of file
diff --git a/part1_example_cirq.ipynb b/part1_example_cirq.ipynb
deleted file mode 100644
index c3d383b..0000000
--- a/part1_example_cirq.ipynb
+++ /dev/null
@@ -1,226 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9c97e0dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7f19ddcc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a443b6a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1f4921cf250>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cb2031cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#submission to part 1, you should make this into a .py file\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "def encode(image):\n",
-    "    circuit=cirq.Circuit()\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))\n",
-    "    return circuit\n",
-    "\n",
-    "def decode(histogram):\n",
-    "    if 1 in histogram.keys():\n",
-    "        image=[[0,0],[0,0]]\n",
-    "    else:\n",
-    "        image=[[1,1],[1,1]]\n",
-    "    return image\n",
-    "\n",
-    "def run_part1(image):\n",
-    "    #encode image into a circuit\n",
-    "    circuit=encode(data['image'])\n",
-    "\n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "\n",
-    "    #reconstruct the image\n",
-    "    image_re=decode(histogram)\n",
-    "\n",
-    "    return circuit,image_re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "385faa44",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit,image_re=run_part1(data['image'])\n",
-    "    \n",
-    "    #count the number of 2qubit gates used\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(data['image'],image_re)\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1 \n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad7e81d7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/part1_example_qiskit.ipynb b/part1_example_qiskit.ipynb
deleted file mode 100644
index 90b2c18..0000000
--- a/part1_example_qiskit.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qiskit\n",
-    "from qiskit import quantum_info\n",
-    "from qiskit.execute_function import execute\n",
-    "from qiskit import BasicAer\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from typing import Dict, List\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: qiskit.QuantumCircuit) -> dict:\n",
-    "    \"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\n",
-    "    backend = BasicAer.get_backend('statevector_simulator')\n",
-    "    job = execute(circuit, backend)\n",
-    "    result = job.result()\n",
-    "    state_vector = result.get_statevector()\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "\n",
-    "def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:\n",
-    "    \"\"\"Returns the number of gate operations with each number of qubits.\"\"\"\n",
-    "    counter = Counter([len(gate[1]) for gate in circuit.data])\n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n",
-    "\n",
-    "def image_mse(image1,image2):\n",
-    "    # Using sklearns mean squared error:\n",
-    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1318dcb80>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#submission to part 1, you should make this into a .py file\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "# Functions 'encode' and 'decode' are dummy.\n",
-    "def encode(image):\n",
-    "    q = qiskit.QuantumRegister(3)\n",
-    "    circuit = qiskit.QuantumCircuit(q)\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.rx(np.pi,0)\n",
-    "    return circuit\n",
-    "\n",
-    "def decode(histogram):\n",
-    "    if 1 in histogram.keys():\n",
-    "        image=[[0,0],[0,0]]\n",
-    "    else:\n",
-    "        image=[[1,1],[1,1]]\n",
-    "    return image\n",
-    "\n",
-    "def run_part1(image):\n",
-    "    #encode image into a circuit\n",
-    "    circuit=encode(image)\n",
-    "\n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "\n",
-    "    #reconstruct the image\n",
-    "    image_re=decode(histogram)\n",
-    "\n",
-    "    return circuit,image_re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "n=len(dataset)\n",
-    "mse=0\n",
-    "gatecount=0\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #encode image into circuit\n",
-    "    circuit,image_re=run_part1(data['image'])\n",
-    "    \n",
-    "    #count the number of 2qubit gates used\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #calculate mse\n",
-    "    mse+=image_mse(data['image'],image_re)\n",
-    "    \n",
-    "#fidelity of reconstruction\n",
-    "f=1-mse\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "#score for part1 \n",
-    "print(f*(0.999**gatecount))"
-   ]
-  },
-  {
-   "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": "3.9.15 (main, Oct 11 2022, 22:27:25) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/part2.pickle b/part2.pickle
deleted file mode 100644
index 3065aa7..0000000
Binary files a/part2.pickle and /dev/null differ
diff --git a/part2.qasm b/part2.qasm
deleted file mode 100644
index bad476c..0000000
--- a/part2.qasm
+++ /dev/null
@@ -1,4 +0,0 @@
-OPENQASM 2.0;
-include "qelib1.inc";
-qreg q8[3];
-rx(pi) q8[0];
diff --git a/part2_example_cirq.ipynb b/part2_example_cirq.ipynb
deleted file mode 100644
index 2bb7a6d..0000000
--- a/part2_example_cirq.ipynb
+++ /dev/null
@@ -1,221 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5519a2aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cirq\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: cirq.Circuit) -> dict:\n",
-    "    \"\"\"This funcion simulate a cirq circuit (without measurement) and output results in the format of histogram.\n",
-    "    \"\"\"\n",
-    "    simulator = cirq.Simulator()\n",
-    "    result = simulator.simulate(circuit)\n",
-    "    \n",
-    "    state_vector=result.final_state_vector\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "def count_gates(circuit: cirq.Circuit):\n",
-    "    \"\"\"Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates....\"\"\"\n",
-    "    counter=Counter([len(op.qubits) for op in circuit.all_operations()])\n",
-    "    \n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "11a77286",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dfbb6735",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x18ddbd011c0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "66e9a047",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#you submission\n",
-    "\n",
-    "def encode(image):\n",
-    "    circuit=cirq.Circuit()\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))\n",
-    "    return circuit\n",
-    "\n",
-    "\n",
-    "def run_part2(image):\n",
-    "\n",
-    "    #loade the quantum classifier circuit\n",
-    "    with open('part2.pickle', 'rb') as f:\n",
-    "        classifier=pickle.load(f)\n",
-    "    \n",
-    "    #encode image into circuit\n",
-    "    circuit=encode(image)\n",
-    "    \n",
-    "    #append with classifier circuit\n",
-    "    \n",
-    "    circuit.append(classifier)\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(circuit)\n",
-    "        \n",
-    "    #convert histogram to category\n",
-    "    label=histogram_to_category(histogram)\n",
-    "        \n",
-    "    return circuit,label\n",
-    "#score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "18e6015f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "score=0\n",
-    "gatecount=0\n",
-    "n=len(dataset)\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #run part 2\n",
-    "    circuit,label=run_part2(data['image'])\n",
-    "    \n",
-    "    #count the gate used in the circuit for score calculation\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #check label\n",
-    "    if label==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a73d2810",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/part2_example_qiskit.ipynb b/part2_example_qiskit.ipynb
deleted file mode 100644
index 8b48841..0000000
--- a/part2_example_qiskit.ipynb
+++ /dev/null
@@ -1,230 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qiskit\n",
-    "from qiskit import quantum_info\n",
-    "from qiskit.execute_function import execute\n",
-    "from qiskit import BasicAer\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import json\n",
-    "import os\n",
-    "from collections import Counter\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from typing import Dict, List\n",
-    "\n",
-    "#define utility functions\n",
-    "\n",
-    "def simulate(circuit: qiskit.QuantumCircuit) -> dict:\n",
-    "    \"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\n",
-    "    backend = BasicAer.get_backend('statevector_simulator')\n",
-    "    job = execute(circuit, backend)\n",
-    "    result = job.result()\n",
-    "    state_vector = result.get_statevector()\n",
-    "    \n",
-    "    histogram = dict()\n",
-    "    for i in range(len(state_vector)):\n",
-    "        population = abs(state_vector[i]) ** 2\n",
-    "        if population > 1e-9:\n",
-    "            histogram[i] = population\n",
-    "    \n",
-    "    return histogram\n",
-    "\n",
-    "\n",
-    "def histogram_to_category(histogram):\n",
-    "    \"\"\"This function take a histogram representations of circuit execution results, and process into labels as described in \n",
-    "    the problem description.\"\"\"\n",
-    "    assert abs(sum(histogram.values())-1)<1e-8\n",
-    "    positive=0\n",
-    "    for key in histogram.keys():\n",
-    "        digits = bin(int(key))[2:].zfill(20)\n",
-    "        if digits[-1]=='0':\n",
-    "            positive+=histogram[key]\n",
-    "        \n",
-    "    return positive\n",
-    "\n",
-    "\n",
-    "def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:\n",
-    "    \"\"\"Returns the number of gate operations with each number of qubits.\"\"\"\n",
-    "    counter = Counter([len(gate[1]) for gate in circuit.data])\n",
-    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
-    "    #for k>2\n",
-    "    for i in range(2,20):\n",
-    "        assert counter[i]==0\n",
-    "        \n",
-    "    return counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1295ae250>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
-    "#you can visualize it\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(images[1100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#your submission\n",
-    "\n",
-    "# Functions 'encode' and 'decode' are dummy.\n",
-    "def encode(image):\n",
-    "    q = qiskit.QuantumRegister(3)\n",
-    "    circuit = qiskit.QuantumCircuit(q)\n",
-    "    if image[0][0]==0:\n",
-    "        circuit.rx(np.pi,0)\n",
-    "    return circuit\n",
-    "\n",
-    "\n",
-    "def run_part2(image):\n",
-    "\n",
-    "    #loade the quantum classifier circuit\n",
-    "    classifier=qiskit.QuantumCircuit.from_qasm_file('part2.qasm')\n",
-    "    \n",
-    "    #encode image into circuit\n",
-    "    circuit=encode(image)\n",
-    "    \n",
-    "    #append with classifier circuit\n",
-    "    nq1 = circuit.width()\n",
-    "    nq2 = classifier.width()\n",
-    "    nq = max(nq1, nq2)\n",
-    "    qc = qiskit.QuantumCircuit(nq)\n",
-    "    qc.append(circuit.to_instruction(), list(range(nq1)))\n",
-    "    qc.append(classifier.to_instruction(), list(range(nq2)))\n",
-    "    \n",
-    "    #simulate circuit\n",
-    "    histogram=simulate(qc)\n",
-    "        \n",
-    "    #convert histogram to category\n",
-    "    label=histogram_to_category(histogram)\n",
-    "        \n",
-    "    return circuit,label\n",
-    "#score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#how we grade your submission\n",
-    "\n",
-    "score=0\n",
-    "gatecount=0\n",
-    "n=len(dataset)\n",
-    "\n",
-    "for data in dataset:\n",
-    "    #run part 2\n",
-    "    circuit,label=run_part2(data['image'])\n",
-    "    \n",
-    "    #count the gate used in the circuit for score calculation\n",
-    "    gatecount+=count_gates(circuit)[2]\n",
-    "    \n",
-    "    #check label\n",
-    "    if label==data['category']:\n",
-    "        score+=1\n",
-    "#score\n",
-    "score=score/n\n",
-    "gatecount=gatecount/n\n",
-    "\n",
-    "print(score*(0.999**gatecount))"
-   ]
-  },
-  {
-   "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": "3.9.15"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/quantum_classifier.qasm b/quantum_classifier.qasm
new file mode 100644
index 0000000..d7c0947
--- /dev/null
+++ b/quantum_classifier.qasm
@@ -0,0 +1,5 @@
+OPENQASM 2.0;
+include "qelib1.inc";
+gate gate_RealAmplitudes(param0,param1,param2,param3,param4,param5,param6,param7,param8,param9,param10,param11,param12,param13,param14,param15,param16,param17,param18,param19,param20,param21,param22,param23) q0,q1,q2,q3 { ry(0.555621819228932) q0; ry(1.14319132801287) q1; ry(1.07379590145389) q2; ry(0.409370781763752) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.0165973010020706) q0; ry(0.508652978720279) q1; ry(0.300244419920286) q2; ry(0.136434104028988) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(-0.0649716942665253) q0; ry(0.577026156276957) q1; ry(0.13664098460932) q2; ry(1.24139060584453) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.455662122094092) q0; ry(0.642944089110158) q1; ry(0.570141938819558) q2; ry(0.0393162465417179) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.872843078509701) q0; ry(0.497896034397818) q1; ry(0.792675492672247) q2; ry(0.0302332873841969) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.448717981940666) q0; ry(1.52403189005694) q1; ry(0.877796397476269) q2; ry(0.508643300858681) q3; }
+qreg q[4];
+gate_RealAmplitudes(0.555621819228932,1.14319132801287,1.07379590145389,0.409370781763752,0.0165973010020706,0.508652978720279,0.300244419920286,0.136434104028988,-0.0649716942665253,0.577026156276957,0.13664098460932,1.24139060584453,0.455662122094092,0.642944089110158,0.570141938819558,0.0393162465417179,0.872843078509701,0.497896034397818,0.792675492672247,0.0302332873841969,0.448717981940666,1.52403189005694,0.877796397476269,0.508643300858681) q[0],q[1],q[2],q[3];
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..678658c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,367 @@
+absl-py==1.4.0
+aiofile==3.8.1
+aiohttp==3.8.3
+aiosignal==1.3.1
+alembic @ file:///home/conda/feedstock_root/build_artifacts/alembic_1647367721563/work
+amazon-braket-default-simulator==1.11.4
+amazon-braket-ocean-plugin==1.0.11
+amazon-braket-pennylane-plugin==1.10.4
+amazon-braket-schemas==1.14.0
+amazon-braket-sdk==1.35.3
+antlr4-python3-runtime==4.9.2
+anyio==3.6.2
+appdirs==1.4.4
+appnope==0.1.3
+argcomplete==2.0.0
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1618968359944/work
+astunparse==1.6.3
+async-generator==1.10
+async-timeout==4.0.2
+attrs==20.3.0
+autograd==1.5
+autoray==0.6.0
+awscli==1.27.57
+azure-core==1.26.2
+azure-identity==1.12.0
+azure-quantum @ file:///home/vsts_azpcontainer/conda-bld/qsharp_1670279498240/work/src/drops/wheels/azure_quantum-0.27.244707-py3-none-any.whl
+azure-storage-blob==12.14.1
+Babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1619719576210/work
+backcall==0.2.0
+backoff==2.2.1
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work
+beautifulsoup4==4.11.1
+bleach==6.0.0
+blinker==1.4
+bokeh==3.0.3
+boltons==21.0.0
+boto3==1.26.57
+botocore==1.29.57
+brotlipy @ file:///home/conda/feedstock_root/build_artifacts/brotlipy_1636012194889/work
+cached-property==1.5.2
+cachetools==4.2.4
+caio==0.9.11
+certifi==2022.12.7
+certipy==0.1.3
+cffi==1.15.1
+chardet==3.0.4
+charset-normalizer==3.0.1
+cirq==1.1.0
+cirq-aqt==1.1.0
+cirq-core==1.1.0
+cirq-google==1.1.0
+cirq-ionq==1.1.0
+cirq-pasqal==1.1.0
+cirq-rigetti==1.1.0
+cirq-web==1.1.0
+click==8.1.3
+code-snippets @ file:///tmp/qbraid/wheels/code_snippets-0.1.2-py3-none-any.whl
+colorama==0.4.6
+comm==0.1.2
+conda==4.11.0
+conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1636021710069/work
+configobj==5.0.8
+contourpy==1.0.7
+cryptography==39.0.0
+cycler==0.11.0
+Cython==0.29.33
+debugpy==1.6.6
+decorator==5.1.1
+defusedxml==0.7.1
+Deprecated==1.2.13
+deprecation==2.1.0
+dill==0.3.5.1
+dimod==0.12.3
+diskcache==5.4.0
+dlx==1.0.4
+docplex==2.25.236
+docs-react-widget @ file:///tmp/qbraid/wheels/docs_react_widget-0.1.0-py3-none-any.whl
+docutils==0.16
+duet==0.2.7
+dwave-cloud-client==0.10.4
+dwave-greedy==0.3.0
+dwave-hybrid==0.6.10
+dwave-inspector==0.4.0
+dwave-neal==0.6.0
+dwave-networkx==0.8.13
+dwave-ocean-sdk==6.2.0
+dwave-preprocessing==0.5.4
+dwave-samplers==1.0.0
+dwave-system==1.18.0
+dwave-tabu==0.5.0
+dwavebinarycsp==0.2.0
+entrypoints==0.4
+environment-manager @ file:///tmp/qbraid/wheels/environment_manager-0.1.4-py3-none-any.whl
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1646044401614/work
+fastdtw==0.3.4
+fasteners==0.18
+fastjsonschema==2.16.2
+Flask==2.2.2
+flatbuffers==23.1.21
+flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1645629044586/work/source/flit_core
+fonttools==4.38.0
+frozendict==2.3.4
+frozenlist==1.3.3
+future==0.18.3
+gast==0.4.0
+gitdb==4.0.10
+GitPython==3.1.30
+google-api-core==1.34.0
+google-api-python-client==2.31.0
+google-auth==2.16.0
+google-auth-httplib2==0.1.0
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+googleapis-common-protos==1.58.0
+graphviz==0.20.1
+greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1635836790477/work
+grpcio==1.51.1
+grpcio-status==1.48.2
+h11==0.14.0
+h5py==3.2.1
+homebase==1.0.1
+html5lib==1.1
+httpcore==0.16.3
+httplib2==0.21.0
+httpx==0.23.3
+ibm-cloud-sdk-core==3.16.1
+ibm-platform-services==0.31.0
+idna==2.8
+imageio==2.25.0
+importlib-metadata==6.0.0
+importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1635615662634/work
+inflection==0.5.1
+ipykernel==6.20.2
+ipyparallel==8.4.1
+ipython==8.8.0
+ipython-genutils==0.2.0
+ipyvue==1.8.0
+ipyvuetify==1.8.4
+ipywidgets==8.0.4
+iso8601==1.1.0
+isodate==0.6.1
+itsdangerous==2.1.2
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1637175083648/work
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.2.0
+json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1600692310011/work
+jsonref==1.1.0
+jsonschema==4.17.3
+jupyter-bokeh==3.0.5
+jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1642858610849/work
+jupyter-server==1.23.5
+jupyter-server-mathjax==0.2.6
+jupyter-telemetry @ file:///home/conda/feedstock_root/build_artifacts/jupyter_telemetry_1605173804246/work
+jupyter_core==5.1.5
+jupyterhub @ file:///home/conda/feedstock_root/build_artifacts/jupyterhub-feedstock_1647267198467/work
+jupyterlab==3.5.3
+jupyterlab-git==0.41.0
+jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1601375948261/work
+jupyterlab-server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1641592475363/work
+jupyterlab-widgets==3.0.5
+keras==2.11.0
+keras-nightly==2.12.0.dev2023012508
+Keras-Preprocessing==1.1.2
+kiwisolver==1.4.4
+lab-main-menu @ file:///tmp/qbraid/wheels/lab_main_menu-0.1.1-py3-none-any.whl
+lark==0.11.3
+lark-parser==0.12.0
+libclang==15.0.6.1
+libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1644831758535/work/libmambapy
+lxml==4.9.2
+Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1646959760357/work
+mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1644831758535/work/mamba
+Markdown==3.4.1
+MarkupSafe==2.1.2
+matplotlib==3.6.0
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1631080358261/work
+minorminer==0.2.10
+mistune==2.0.4
+more-itertools==9.0.0
+mpltools==0.2.0
+mpmath==1.2.1
+msal==1.20.0
+msal-extensions==1.0.0
+msgpack==0.6.2
+msrest==0.7.1
+mthree==1.1.0
+multidict==6.0.4
+multitasking==0.0.11
+mypy-extensions==0.4.3
+Nasdaq-Data-Link==1.0.4
+nbclassic==0.4.8
+nbclient==0.7.2
+nbconvert==7.2.9
+nbdime==3.1.1
+nbformat==5.7.3
+nest-asyncio==1.5.6
+networkx==2.8.8
+ninja==1.11.1
+notebook==6.5.2
+notebook-shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1646330736330/work
+ntlm-auth==1.5.0
+numpy==1.21.6
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+oauth2client==4.1.3
+oauthlib==3.2.2
+opencv-python==4.7.0.68
+openfermion==1.5.1
+openpulse==0.4.1
+openqasm3==0.4.0
+opt-einsum==3.3.0
+oqpy==0.1.2
+orjson==3.8.5
+packaging==23.0
+pamela==1.0.0
+pandas==1.5.3
+pandocfilters==1.5.0
+parso==0.8.3
+pbr==5.11.1
+penaltymodel==1.0.2
+PennyLane==0.27.0
+PennyLane-Cirq==0.28.0
+PennyLane-Lightning==0.28.0
+PennyLane-qiskit==0.28.0
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.4.0
+platformdirs==2.6.2
+plucky==0.4.3
+ply==3.11
+portalocker==2.7.0
+prometheus-client==0.16.0
+prompt-toolkit==3.0.36
+proto-plus==1.22.2
+protobuf==3.19.6
+psutil==5.9.4
+ptyprocess==0.7.0
+PubChemPy==1.0.4
+pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
+py==1.11.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pybind11==2.10.3
+pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1636020362356/work
+pycparser==2.21
+pycurl==7.45.1
+pydantic==1.10.4
+Pygments==2.14.0
+PyJWT==2.6.0
+pylatexenc==2.10
+pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1643496850550/work
+pyparsing==3.0.9
+pyqir-generator==0.4.2a1
+pyqubo==1.4.0
+pyquil==3.3.2
+pyrsistent==0.19.3
+pyscf==2.1.1
+PySocks==1.7.1
+python-constraint==1.4.0
+python-dateutil==2.8.2
+python-json-logger @ file:///home/conda/feedstock_root/build_artifacts/python-json-logger_1602545356084/work
+python-markdown-math==0.8
+python-rapidjson==1.9
+pytket==1.11.1
+pytket-cirq==0.28.0
+pytket-qiskit==0.34.0
+pytz==2022.7.1
+PyYAML==5.4.1
+pyzmq==25.0.0
+qcs-api-client==0.21.2
+qdk @ file:///home/vsts_azpcontainer/conda-bld/qsharp_1670279498240/work/src/drops/wheels/qdk-0.27.244707-py3-none-any.whl
+qickstart-graders @ git+https://github.com/TheGupta2012/QickStart.git@65501cc7d5e98506b90e58e43d8d4a2aee551739
+QInfer==1.0
+qiskit==0.39.5
+qiskit-aer==0.11.2
+qiskit-aqua==0.9.5
+qiskit-finance==0.3.4
+qiskit-ibm-runtime==0.8.0
+qiskit-ibmq-provider==0.19.2
+qiskit-ignis==0.7.0
+qiskit-ionq==0.3.10
+qiskit-machine-learning==0.5.0
+qiskit-nature==0.5.2
+qiskit-optimization==0.4.0
+qiskit-qir==0.2.0
+qiskit-terra==0.22.4
+qsharp @ file:///home/vsts_azpcontainer/conda-bld/qsharp_1670279498240/work/src/drops/wheels/qsharp-0.27.244707-py3-none-any.whl
+qsharp-chemistry @ file:///home/vsts_azpcontainer/conda-bld/qsharp_1670279498240/work/src/drops/wheels/qsharp_chemistry-0.27.244707-py3-none-any.whl
+qsharp-core @ file:///home/vsts_azpcontainer/conda-bld/qsharp_1670279498240/work/src/drops/wheels/qsharp_core-0.27.244707-py3-none-any.whl
+Quandl==3.7.0
+quantum-jobs @ file:///tmp/qbraid/wheels/quantum_jobs-0.1.1-py3-none-any.whl
+qutip==4.7.1
+qwasm==1.0.1
+rectangle-packer==2.0.1
+requests==2.28.2
+requests-ntlm==1.1.0
+requests-oauthlib==1.3.1
+retry==0.9.2
+retrying==1.3.4
+retworkx==0.12.1
+rfc3339==6.2
+rfc3986==1.5.0
+rpcq==3.10.0
+rsa==4.9
+ruamel-yaml-conda @ file:///home/conda/feedstock_root/build_artifacts/ruamel_yaml_1636009144459/work
+ruamel.yaml==0.17.21
+ruamel.yaml.clib==0.2.7
+rustworkx==0.12.1
+s3transfer==0.6.0
+scikit-learn==1.2.1
+scipy==1.10.0
+seaborn==0.12.2
+semantic-version==2.10.0
+Send2Trash==1.8.0
+six==1.16.0
+smmap==5.0.0
+sniffio==1.3.0
+sortedcontainers==2.4.0
+soupsieve==2.3.2.post1
+SQLAlchemy @ file:///home/conda/feedstock_root/build_artifacts/sqlalchemy_1646615291967/work
+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1644872665635/work
+stevedore==4.1.1
+symengine==0.9.2
+sympy==1.11.1
+tensorboard==2.11.2
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.11.0
+tensorflow-estimator==2.11.0
+tensorflow-io-gcs-filesystem==0.30.0
+termcolor==2.2.0
+terminado==0.17.1
+testpath==0.6.0
+threadpoolctl==3.1.0
+tinycss2==1.2.1
+toml==0.10.2
+tomli==2.0.1
+torch==1.13.1
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.8.1
+tweedledum==1.1.1
+types-Jinja2==2.11.9
+types-MarkupSafe==1.1.10
+types-pkg-resources==0.1.3
+types-python-dateutil==2.8.19.6
+types-retry==0.9.9.1
+typing_extensions==4.4.0
+ui-tweaks @ file:///tmp/qbraid/wheels/ui_tweaks-0.1.1-py3-none-any.whl
+uritemplate==4.1.1
+urllib3==1.26.14
+wcwidth==0.2.6
+webencodings==0.5.1
+websocket-client==1.3.3
+websockets==10.4
+Werkzeug==2.2.2
+widgetsnbextension==4.0.5
+wrapt==1.14.1
+xyzservices==2022.9.0
+yarl==1.8.2
+yfinance==0.2.6
+zipp==3.11.0
diff --git a/test.ipynb b/test.ipynb
new file mode 100644
index 0000000..bf9c8a7
--- /dev/null
+++ b/test.ipynb
@@ -0,0 +1,328 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e2e5bcd9-2cb8-465d-b166-23128574b2c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2000/2000 [00:00<00:00, 23292.31it/s]\n",
+      "100%|██████████| 10/10 [00:01<00:00,  7.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9379616325363924\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  6.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9379616325363924,0.3944362547996004,./data\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qiskit\n",
+    "from qiskit import quantum_info, QuantumCircuit\n",
+    "from qiskit.execute_function import execute\n",
+    "from qiskit import BasicAer\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "from collections import Counter\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from typing import Dict, List\n",
+    "from sklearn.decomposition import PCA\n",
+    "import tqdm\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "if len(sys.argv) > 1:\n",
+    "    data_path = sys.argv[1]\n",
+    "else:\n",
+    "    data_path = '.'\n",
+    "\n",
+    "#define utility functions\n",
+    "\n",
+    "def simulate(circuit: qiskit.QuantumCircuit) -> dict:\n",
+    "    \"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\n",
+    "    backend = BasicAer.get_backend('statevector_simulator')\n",
+    "    job = execute(circuit, backend)\n",
+    "    result = job.result()\n",
+    "    state_vector = result.get_statevector()\n",
+    "    \n",
+    "    histogram = dict()\n",
+    "    for i in range(len(state_vector)):\n",
+    "        population = abs(state_vector[i]) ** 2\n",
+    "        if population > 1e-9:\n",
+    "            histogram[i] = population\n",
+    "    \n",
+    "    return histogram\n",
+    "\n",
+    "\n",
+    "def histogram_to_category(histogram):\n",
+    "    \"\"\"This function takes a histogram representation of circuit execution results, and processes into labels as described in\n",
+    "    the problem description.\"\"\"\n",
+    "    assert abs(sum(histogram.values())-1)<1e-8\n",
+    "    positive=0\n",
+    "    for key in histogram.keys():\n",
+    "        digits = bin(int(key))[2:].zfill(20)\n",
+    "        if digits[-1]=='0':\n",
+    "            positive+=histogram[key]\n",
+    "        \n",
+    "    return positive\n",
+    "\n",
+    "def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:\n",
+    "    \"\"\"Returns the number of gate operations with each number of qubits.\"\"\"\n",
+    "    counter = Counter([len(gate[1]) for gate in circuit.data])\n",
+    "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
+    "    #for k>2\n",
+    "    for i in range(3,20):\n",
+    "        assert counter[i]==0\n",
+    "        \n",
+    "    return counter\n",
+    "\n",
+    "\n",
+    "def image_mse(image1,image2):\n",
+    "    # Using sklearns mean squared error:\n",
+    "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
+    "    return mean_squared_error(255*image1,255*image2)\n",
+    "\n",
+    "# [normalize]\n",
+    "def normalize(row):\n",
+    "    #We calculate the squareroot of the sum of the square values of the row\n",
+    "    normalization_factor = np.sqrt(np.sum(row**2)) \n",
+    "    if normalization_factor == 0.:\n",
+    "        #If the sum is zero we return a 0\n",
+    "        return 0.0\n",
+    "    #Else we divide each value between the sum value above\n",
+    "    row = row / normalization_factor\n",
+    "    return row, normalization_factor\n",
+    "#\n",
+    "data_path = \"./data\"\n",
+    "\n",
+    "def test():\n",
+    "    #load the actual hackthon data (fashion-mnist)\n",
+    "    images=np.load(data_path+'/images.npy')\n",
+    "    labels=np.load(data_path+'/labels.npy')\n",
+    "    \n",
+    "    #test part 1\n",
+    "    global pca, AMPLITUDE_ENCODING_N_QUBITS,IMAGE_SIZE, dataset\n",
+    "\n",
+    "    AMPLITUDE_ENCODING_N_QUBITS = 4\n",
+    "    IMAGE_SIZE = 28\n",
+    "    N_IMAGES = len(images)\n",
+    "\n",
+    "    pca = PCA(n_components=2**AMPLITUDE_ENCODING_N_QUBITS)\n",
+    "    \n",
+    "    data = images.reshape(N_IMAGES, IMAGE_SIZE * IMAGE_SIZE)\n",
+    "    pca.fit(data)\n",
+    "    \n",
+    "    # Apply dimensionality reduction on your data\n",
+    "    images_pca = pca.transform(data)\n",
+    "    \n",
+    "    dataset = []\n",
+    "    for i in tqdm.tqdm(range(len(images))):\n",
+    "        image_pca = images_pca[i]\n",
+    "        image_pca_min_ = image_pca.min()\n",
+    "        image_pca_positive = image_pca - image_pca_min_\n",
+    "        \n",
+    "        dataset_i = {}\n",
+    "        \n",
+    "        normalized_state, normalization_factor = normalize(image_pca_positive)\n",
+    "        \n",
+    "        dataset_i[\"image\"] = images[i]\n",
+    "        dataset_i[\"image_vector\"] = normalized_state\n",
+    "        dataset_i[\"label\"] = labels[i]\n",
+    "        dataset_i[\"normalization_factor\"] = normalization_factor\n",
+    "        dataset_i[\"pca_min_\"] = image_pca_min_\n",
+    "        \n",
+    "        dataset.append(dataset_i)\n",
+    "    \n",
+    "    n=len(images)\n",
+    "    mse=0\n",
+    "    gatecount=0\n",
+    "    n = 10 # sanity check to test that code works\n",
+    "    \n",
+    "    for i in tqdm.tqdm(range(n)):\n",
+    "        #encode image into circuit\n",
+    "        circuit,image_re=run_part1(dataset[i][\"image_vector\"])\n",
+    "        image_re = np.asarray(image_re)\n",
+    "\n",
+    "        #count the number of 2qubit gates used\n",
+    "        gatecount += count_gates(circuit)[2]\n",
+    "\n",
+    "        #calculate mse\n",
+    "        mse+=image_mse(dataset[i][\"image\"], image_re)\n",
+    "\n",
+    "    #fidelity of reconstruction\n",
+    "    f=1-mse/n\n",
+    "    gatecount=gatecount/n\n",
+    "\n",
+    "    #score for part1\n",
+    "    score_part1=f*(0.999**gatecount)\n",
+    "    \n",
+    "    print(score_part1)\n",
+    "\n",
+    "    #test part 2\n",
+    "    \n",
+    "    score=0\n",
+    "    gatecount=0\n",
+    "    n=len(images)\n",
+    "    \n",
+    "    n = 10\n",
+    "    for i in tqdm.tqdm(range(n)):\n",
+    "        #run part 2\n",
+    "        circuit,label=run_part2(dataset[i * 100][\"image_vector\"])\n",
+    "\n",
+    "        #count the gate used in the circuit for score calculation\n",
+    "        gatecount+=count_gates(circuit)[2]\n",
+    "\n",
+    "        #check label\n",
+    "        if label==labels[i * 100]:\n",
+    "            score+=1\n",
+    "    \n",
+    "    #score\n",
+    "    score=score/n\n",
+    "    gatecount=gatecount/n\n",
+    "\n",
+    "    score_part2=score*(0.999**gatecount)\n",
+    "    \n",
+    "    print(score_part1, \",\", score_part2, \",\", data_path, sep=\"\")\n",
+    "\n",
+    "\n",
+    "############################\n",
+    "#      YOUR CODE HERE      #\n",
+    "############################\n",
+    "def encode(image):\n",
+    "    qc = QuantumCircuit(AMPLITUDE_ENCODING_N_QUBITS)\n",
+    "\n",
+    "    qc.initialize(image)\n",
+    "    \n",
+    "    for i in range(AMPLITUDE_ENCODING_N_QUBITS + 2):\n",
+    "        qc = qc.decompose()\n",
+    "        \n",
+    "    return qc\n",
+    "\n",
+    "def decode(histogram):\n",
+    "    histogram_array = np.zeros(2 ** AMPLITUDE_ENCODING_N_QUBITS)\n",
+    "    for i in range(2 ** AMPLITUDE_ENCODING_N_QUBITS):\n",
+    "        histogram_array[i] = histogram.get(i, 0)\n",
+    "        \n",
+    "    root = np.sqrt(histogram_array)\n",
+    "    \n",
+    "    root = root * dataset[i][\"normalization_factor\"]\n",
+    "    \n",
+    "    root = root + dataset[i][\"pca_min_\"]\n",
+    "\n",
+    "    reconstruction = pca.inverse_transform([root])\n",
+    "\n",
+    "    \n",
+    "    image = reconstruction.reshape(IMAGE_SIZE, IMAGE_SIZE)\n",
+    "    \n",
+    "    return image\n",
+    "\n",
+    "def run_part1(image):\n",
+    "    #encode image into a circuit\n",
+    "    circuit=encode(image)\n",
+    "\n",
+    "    #simulate circuit\n",
+    "    histogram=simulate(circuit)\n",
+    "\n",
+    "    #reconstruct the image\n",
+    "    image_re=decode(histogram)\n",
+    "\n",
+    "    return circuit,image_re\n",
+    "\n",
+    "def run_part2(image):\n",
+    "    # load the quantum classifier circuit\n",
+    "    classifier=qiskit.QuantumCircuit.from_qasm_file('quantum_classifier.qasm')\n",
+    "    \n",
+    "    #encode image into circuit\n",
+    "    circuit=encode(image)\n",
+    "    \n",
+    "    #append with classifier circuit\n",
+    "    nq1 = circuit.width()\n",
+    "    nq2 = classifier.width()\n",
+    "    nq = max(nq1, nq2)\n",
+    "    qc = qiskit.QuantumCircuit(nq)\n",
+    "    qc.append(circuit.to_instruction(), list(range(nq1)))\n",
+    "    qc.append(classifier.to_instruction(), list(range(nq2)))\n",
+    "    \n",
+    "    #simulate circuit\n",
+    "    histogram=simulate(qc)\n",
+    "        \n",
+    "    #convert histogram to category\n",
+    "    label=histogram_to_category(histogram)\n",
+    "    \n",
+    "    #thresholding the label, any way you want\n",
+    "    if label>0.5:\n",
+    "        label=1\n",
+    "    else:\n",
+    "        label=0\n",
+    "        \n",
+    "    return circuit,label\n",
+    "\n",
+    "############################\n",
+    "#      END YOUR CODE       #\n",
+    "############################\n",
+    "\n",
+    "test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21a93cc3-13b1-40bc-85fa-6e5bfd5ee403",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 [Default]",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..21d0d05
--- /dev/null
+++ b/test.py
@@ -0,0 +1,245 @@
+import qiskit
+from qiskit import quantum_info, QuantumCircuit
+from qiskit.execute_function import execute
+from qiskit import BasicAer
+import numpy as np
+import pickle
+import json
+import os
+import sys
+from collections import Counter
+from sklearn.metrics import mean_squared_error
+from typing import Dict, List
+from sklearn.decomposition import PCA
+import tqdm
+
+
+import matplotlib.pyplot as plt
+
+if len(sys.argv) > 1:
+    data_path = sys.argv[1]
+else:
+    data_path = '.'
+
+#define utility functions
+
+def simulate(circuit: qiskit.QuantumCircuit) -> dict:
+    """Simulate the circuit, give the state vector as the result."""
+    backend = BasicAer.get_backend('statevector_simulator')
+    job = execute(circuit, backend)
+    result = job.result()
+    state_vector = result.get_statevector()
+    
+    histogram = dict()
+    for i in range(len(state_vector)):
+        population = abs(state_vector[i]) ** 2
+        if population > 1e-9:
+            histogram[i] = population
+    
+    return histogram
+
+
+def histogram_to_category(histogram):
+    """This function takes a histogram representation of circuit execution results, and processes into labels as described in
+    the problem description."""
+    assert abs(sum(histogram.values())-1)<1e-8
+    positive=0
+    for key in histogram.keys():
+        digits = bin(int(key))[2:].zfill(20)
+        if digits[-1]=='0':
+            positive+=histogram[key]
+        
+    return positive
+
+def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:
+    """Returns the number of gate operations with each number of qubits."""
+    counter = Counter([len(gate[1]) for gate in circuit.data])
+    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit
+    #for k>2
+    for i in range(3,20):
+        assert counter[i]==0
+        
+    return counter
+
+
+def image_mse(image1,image2):
+    # Using sklearns mean squared error:
+    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
+    return mean_squared_error(255*image1,255*image2)
+
+# [normalize]
+def normalize(row):
+    #We calculate the squareroot of the sum of the square values of the row
+    normalization_factor = np.sqrt(np.sum(row**2)) 
+    if normalization_factor == 0.:
+        #If the sum is zero we return a 0
+        return 0.0
+    #Else we divide each value between the sum value above
+    row = row / normalization_factor
+    return row, normalization_factor
+
+def test():
+    #load the actual hackthon data (fashion-mnist)
+    images=np.load(data_path+'/images.npy')
+    labels=np.load(data_path+'/labels.npy')
+    
+    #test part 1
+    global pca, AMPLITUDE_ENCODING_N_QUBITS,IMAGE_SIZE, dataset
+
+    AMPLITUDE_ENCODING_N_QUBITS = 4
+    IMAGE_SIZE = 28
+    N_IMAGES = len(images)
+
+    pca = PCA(n_components=2**AMPLITUDE_ENCODING_N_QUBITS)
+    
+    data = images.reshape(N_IMAGES, IMAGE_SIZE * IMAGE_SIZE)
+    pca.fit(data)
+    
+    # Apply dimensionality reduction on your data
+    images_pca = pca.transform(data)
+    
+    dataset = []
+    for i in tqdm.tqdm(range(len(images))):
+        image_pca = images_pca[i]
+        image_pca_min_ = image_pca.min()
+        image_pca_positive = image_pca - image_pca_min_
+        
+        dataset_i = {}
+        
+        normalized_state, normalization_factor = normalize(image_pca_positive)
+        
+        dataset_i["image"] = images[i]
+        dataset_i["image_vector"] = normalized_state
+        dataset_i["label"] = labels[i]
+        dataset_i["normalization_factor"] = normalization_factor
+        dataset_i["pca_min_"] = image_pca_min_
+        
+        dataset.append(dataset_i)
+    
+    n=len(images)
+    mse=0
+    gatecount=0
+    
+    for i in tqdm.tqdm(range(n)):
+        #encode image into circuit
+        circuit,image_re=run_part1(dataset[i]["image_vector"])
+        image_re = np.asarray(image_re)
+
+        #count the number of 2qubit gates used
+        gatecount += count_gates(circuit)[2]
+
+        #calculate mse
+        mse+=image_mse(dataset[i]["image"], image_re)
+
+    #fidelity of reconstruction
+    f=1-mse/n
+    gatecount=gatecount/n
+
+    #score for part1
+    score_part1=f*(0.999**gatecount)
+    
+    print(score_part1)
+
+    #test part 2
+    
+    score=0
+    gatecount=0
+    n=len(images)
+
+    for i in tqdm.tqdm(range(n)):
+        #run part 2
+        circuit,label=run_part2(dataset[i]["image_vector"])
+
+        #count the gate used in the circuit for score calculation
+        gatecount+=count_gates(circuit)[2]
+
+        #check label
+        if label==labels[i]:
+            score+=1
+    
+    #score
+    score=score/n
+    gatecount=gatecount/n
+
+    score_part2=score*(0.999**gatecount)
+    
+    print(score_part1, ",", score_part2, ",", data_path, sep="")
+
+
+############################
+#      YOUR CODE HERE      #
+############################
+def encode(image):
+    qc = QuantumCircuit(AMPLITUDE_ENCODING_N_QUBITS)
+
+    qc.initialize(image)
+    
+    for i in range(AMPLITUDE_ENCODING_N_QUBITS + 2):
+        qc = qc.decompose()
+        
+    return qc
+
+def decode(histogram):
+    histogram_array = np.zeros(2 ** AMPLITUDE_ENCODING_N_QUBITS)
+    for i in range(2 ** AMPLITUDE_ENCODING_N_QUBITS):
+        histogram_array[i] = histogram.get(i, 0)
+        
+    root = np.sqrt(histogram_array)
+    
+    root = root * dataset[i]["normalization_factor"]
+    
+    root = root + dataset[i]["pca_min_"]
+
+    reconstruction = pca.inverse_transform([root])
+
+    
+    image = reconstruction.reshape(IMAGE_SIZE, IMAGE_SIZE)
+    
+    return image
+
+def run_part1(image):
+    #encode image into a circuit
+    circuit=encode(image)
+
+    #simulate circuit
+    histogram=simulate(circuit)
+
+    #reconstruct the image
+    image_re=decode(histogram)
+
+    return circuit,image_re
+
+def run_part2(image):
+    # load the quantum classifier circuit
+    classifier=qiskit.QuantumCircuit.from_qasm_file('quantum_classifier.qasm')
+    
+    #encode image into circuit
+    circuit=encode(image)
+    
+    #append with classifier circuit
+    nq1 = circuit.width()
+    nq2 = classifier.width()
+    nq = max(nq1, nq2)
+    qc = qiskit.QuantumCircuit(nq)
+    qc.append(circuit.to_instruction(), list(range(nq1)))
+    qc.append(classifier.to_instruction(), list(range(nq2)))
+    
+    #simulate circuit
+    histogram=simulate(qc)
+        
+    #convert histogram to category
+    label=histogram_to_category(histogram)
+    
+    #thresholding the label, any way you want
+    if label>0.5:
+        label=1
+    else:
+        label=0
+        
+    return circuit,label
+
+############################
+#      END YOUR CODE       #
+############################
+
+test()
\ No newline at end of file
diff --git a/train_classifier.ipynb b/train_classifier.ipynb
new file mode 100644
index 0000000..6743ceb
--- /dev/null
+++ b/train_classifier.ipynb
@@ -0,0 +1,2694 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install qiskit"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3PB9KaTr2CIt",
+        "outputId": "5288d69d-2245-41cc-ead0-1f0219092a17"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting qiskit\n",
+            "  Downloading qiskit-0.40.0.tar.gz (14 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting qiskit-terra==0.23.0\n",
+            "  Downloading qiskit_terra-0.23.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m67.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting qiskit-aer==0.11.2\n",
+            "  Downloading qiskit_aer-0.11.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.8 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m77.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting qiskit-ibmq-provider==0.19.2\n",
+            "  Downloading qiskit_ibmq_provider-0.19.2-py3-none-any.whl (240 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.4/240.4 KB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.8/dist-packages (from qiskit-aer==0.11.2->qiskit) (1.21.6)\n",
+            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from qiskit-aer==0.11.2->qiskit) (1.7.3)\n",
+            "Collecting websocket-client>=1.0.1\n",
+            "  Downloading websocket_client-1.5.0-py3-none-any.whl (55 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.9/55.9 KB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting websockets>=10.0\n",
+            "  Downloading websockets-10.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (106 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.0/107.0 KB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from qiskit-ibmq-provider==0.19.2->qiskit) (1.24.3)\n",
+            "Collecting requests-ntlm>=1.1.0\n",
+            "  Downloading requests_ntlm-1.1.0-py2.py3-none-any.whl (5.7 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.8/dist-packages (from qiskit-ibmq-provider==0.19.2->qiskit) (2.8.2)\n",
+            "Requirement already satisfied: requests>=2.19 in /usr/local/lib/python3.8/dist-packages (from qiskit-ibmq-provider==0.19.2->qiskit) (2.25.1)\n",
+            "Collecting stevedore>=3.0.0\n",
+            "  Downloading stevedore-4.1.1-py3-none-any.whl (50 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.0/50.0 KB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: dill>=0.3 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra==0.23.0->qiskit) (0.3.6)\n",
+            "Collecting symengine>=0.9\n",
+            "  Downloading symengine-0.9.2-cp38-cp38-manylinux2010_x86_64.whl (37.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.5/37.5 MB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting ply>=3.10\n",
+            "  Downloading ply-3.11-py2.py3-none-any.whl (49 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 KB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra==0.23.0->qiskit) (1.7.1)\n",
+            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra==0.23.0->qiskit) (5.4.8)\n",
+            "Collecting rustworkx>=0.12.0\n",
+            "  Downloading rustworkx-0.12.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m61.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.0->qiskit-ibmq-provider==0.19.2->qiskit) (1.15.0)\n",
+            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.19.2->qiskit) (4.0.0)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.19.2->qiskit) (2.10)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.19.2->qiskit) (2022.12.7)\n",
+            "Collecting cryptography>=1.3\n",
+            "  Downloading cryptography-39.0.0-cp36-abi3-manylinux_2_28_x86_64.whl (4.2 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m95.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting ntlm-auth>=1.0.2\n",
+            "  Downloading ntlm_auth-1.5.0-py2.py3-none-any.whl (29 kB)\n",
+            "Collecting pbr!=2.1.0,>=2.0.0\n",
+            "  Downloading pbr-5.11.1-py2.py3-none-any.whl (112 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.7/112.7 KB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/dist-packages (from sympy>=1.3->qiskit-terra==0.23.0->qiskit) (1.2.1)\n",
+            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.8/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.19.2->qiskit) (1.15.1)\n",
+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.19.2->qiskit) (2.21)\n",
+            "Building wheels for collected packages: qiskit\n",
+            "  Building wheel for qiskit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for qiskit: filename=qiskit-0.40.0-py3-none-any.whl size=12841 sha256=da78fe2389582649fb7eea82e893b93862fa51cb70676cd30c07ddbeed1f956a\n",
+            "  Stored in directory: /root/.cache/pip/wheels/44/52/90/52d837f0b0cc96b59422d8c3a66dd38e8e08660439e513d707\n",
+            "Successfully built qiskit\n",
+            "Installing collected packages: ply, websockets, websocket-client, symengine, rustworkx, pbr, ntlm-auth, stevedore, cryptography, requests-ntlm, qiskit-terra, qiskit-ibmq-provider, qiskit-aer, qiskit\n",
+            "Successfully installed cryptography-39.0.0 ntlm-auth-1.5.0 pbr-5.11.1 ply-3.11 qiskit-0.40.0 qiskit-aer-0.11.2 qiskit-ibmq-provider-0.19.2 qiskit-terra-0.23.0 requests-ntlm-1.1.0 rustworkx-0.12.1 stevedore-4.1.1 symengine-0.9.2 websocket-client-1.5.0 websockets-10.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install qiskit_machine_learning"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 679
+        },
+        "id": "D0bSM9FA21tR",
+        "outputId": "9e8c3eb6-f65d-4b6c-afb3-1e666bf883e1"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting qiskit_machine_learning\n",
+            "  Downloading qiskit_machine_learning-0.5.0-py3-none-any.whl (152 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.8/152.8 KB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: scipy>=1.4 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (1.7.3)\n",
+            "Collecting dill<0.3.6,>=0.3.4\n",
+            "  Downloading dill-0.3.5.1-py2.py3-none-any.whl (95 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.8/95.8 KB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: psutil>=5 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (5.4.8)\n",
+            "Requirement already satisfied: qiskit-terra>=0.22 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (0.23.0)\n",
+            "Requirement already satisfied: setuptools>=40.1.0 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (57.4.0)\n",
+            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (1.0.2)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (1.21.6)\n",
+            "Requirement already satisfied: fastdtw in /usr/local/lib/python3.8/dist-packages (from qiskit_machine_learning) (0.3.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (2.8.2)\n",
+            "Requirement already satisfied: symengine>=0.9 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (0.9.2)\n",
+            "Requirement already satisfied: rustworkx>=0.12.0 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (0.12.1)\n",
+            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (4.1.1)\n",
+            "Requirement already satisfied: ply>=3.10 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (3.11)\n",
+            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.8/dist-packages (from qiskit-terra>=0.22->qiskit_machine_learning) (1.7.1)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.20.0->qiskit_machine_learning) (3.1.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.20.0->qiskit_machine_learning) (1.2.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.0->qiskit-terra>=0.22->qiskit_machine_learning) (1.15.0)\n",
+            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from stevedore>=3.0.0->qiskit-terra>=0.22->qiskit_machine_learning) (5.11.1)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/dist-packages (from sympy>=1.3->qiskit-terra>=0.22->qiskit_machine_learning) (1.2.1)\n",
+            "Installing collected packages: dill, qiskit_machine_learning\n",
+            "  Attempting uninstall: dill\n",
+            "    Found existing installation: dill 0.3.6\n",
+            "    Uninstalling dill-0.3.6:\n",
+            "      Successfully uninstalled dill-0.3.6\n",
+            "Successfully installed dill-0.3.5.1 qiskit_machine_learning-0.5.0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "dill"
+                ]
+              }
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 443
+        },
+        "id": "eXGMb6MT14_8",
+        "outputId": "b6a0b24b-76ea-485a-d2b4-097fbbda2d43"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 2000/2000 [00:00<00:00, 22945.77it/s]\n",
+            "  0%|          | 8/2000 [00:01<07:06,  4.67it/s]\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-3-b75cacfa6645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;31m############################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<ipython-input-3-b75cacfa6645>\u001b[0m in \u001b[0;36mtest\u001b[0;34m()\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;31m#encode image into circuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m         \u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_part1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"image_vector\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m         \u001b[0mimage_re\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_re\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-3-b75cacfa6645>\u001b[0m in \u001b[0;36mrun_part1\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;31m#simulate circuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m     \u001b[0mhistogram\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msimulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;31m#reconstruct the image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-3-b75cacfa6645>\u001b[0m in \u001b[0;36msimulate\u001b[0;34m(circuit)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;34m\"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicAer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'statevector_simulator'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mstate_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_statevector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/execute_function.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(experiments, backend, basis_gates, coupling_map, backend_properties, initial_layout, seed_transpiler, optimization_level, pass_manager, qobj_id, qobj_header, shots, memory, max_credits, seed_simulator, default_qubit_los, default_meas_los, qubit_lo_range, meas_lo_range, schedule_los, meas_level, meas_return, memory_slots, memory_slot_size, rep_time, rep_delay, parameter_binds, schedule_circuit, inst_map, meas_map, scheduling_method, init_qubits, **run_config)\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m         \u001b[0;31m# transpiling the circuits using given transpile options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m         experiments = transpile(\n\u001b[0m\u001b[1;32m    300\u001b[0m             \u001b[0mexperiments\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0mbasis_gates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbasis_gates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpiler.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(circuits, backend, basis_gates, inst_map, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, scheduling_method, instruction_durations, dt, approximation_degree, timing_constraints, seed_transpiler, optimization_level, callback, output_name, unitary_synthesis_method, unitary_synthesis_plugin_config, target, hls_config, init_method, optimization_method, ignore_backend_supplied_default_methods)\u001b[0m\n\u001b[1;32m    379\u001b[0m             \u001b[0mtranspile_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_combine_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshared_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munique_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             output_circuits.append(\n\u001b[0;32m--> 381\u001b[0;31m                 _serial_transpile_circuit(\n\u001b[0m\u001b[1;32m    382\u001b[0m                     \u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m                     \u001b[0mpass_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpiler.py\u001b[0m in \u001b[0;36m_serial_transpile_circuit\u001b[0;34m(circuit, pass_manager, callback, output_name, num_qubits, faulty_qubits_map, backend_prop)\u001b[0m\n\u001b[1;32m    472\u001b[0m     \u001b[0mbackend_prop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m ):\n\u001b[0;32m--> 474\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpass_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfaulty_qubits_map\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         return _remap_circuit_faulty_backend(\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, circuits, output_name, callback)\u001b[0m\n\u001b[1;32m    526\u001b[0m     ) -> Union[QuantumCircuit, List[QuantumCircuit]]:\n\u001b[1;32m    527\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_passmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, circuits, output_name, callback)\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcircuits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQuantumCircuit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_single_circuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_single_circuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py\u001b[0m in \u001b[0;36m_run_single_circuit\u001b[0;34m(self, circuit, output_name, callback)\u001b[0m\n\u001b[1;32m    281\u001b[0m         \"\"\"\n\u001b[1;32m    282\u001b[0m         \u001b[0mrunning_passmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_running_passmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrunning_passmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperty_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrunning_passmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperty_set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, circuit, output_name, callback)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \"\"\"\n\u001b[1;32m    116\u001b[0m         \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mdag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcircuit_to_dag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/converters/circuit_to_dag.py\u001b[0m in \u001b[0;36mcircuit_to_dag\u001b[0;34m(circuit)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0minstruction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         dagcircuit.apply_operation_back(\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstruction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstruction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqubits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstruction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclbits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         )\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__deepcopy__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatch_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/qiskit/circuit/instruction.py\u001b[0m in \u001b[0;36m__deepcopy__\u001b[0;34m(self, _memo)\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0mcpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_definition\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mcpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_definition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_definition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_memo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    170\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m def _reconstruct(x, memo, func, args,\n\u001b[0m\u001b[1;32m    259\u001b[0m                  \u001b[0mstate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlistiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdictiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m                  deepcopy=deepcopy):\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "import qiskit\n",
+        "from qiskit import quantum_info, QuantumCircuit\n",
+        "from qiskit.execute_function import execute\n",
+        "from qiskit import BasicAer\n",
+        "import numpy as np\n",
+        "import pickle\n",
+        "import json\n",
+        "import os\n",
+        "import sys\n",
+        "from collections import Counter\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from typing import Dict, List\n",
+        "from sklearn.decomposition import PCA\n",
+        "import tqdm\n",
+        "\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "if len(sys.argv) > 1:\n",
+        "    data_path = sys.argv[1]\n",
+        "else:\n",
+        "    data_path = '.'\n",
+        "\n",
+        "#define utility functions\n",
+        "\n",
+        "def simulate(circuit: qiskit.QuantumCircuit) -> dict:\n",
+        "    \"\"\"Simulate the circuit, give the state vector as the result.\"\"\"\n",
+        "    backend = BasicAer.get_backend('statevector_simulator')\n",
+        "    job = execute(circuit, backend)\n",
+        "    result = job.result()\n",
+        "    state_vector = result.get_statevector()\n",
+        "    \n",
+        "    histogram = dict()\n",
+        "    for i in range(len(state_vector)):\n",
+        "        population = abs(state_vector[i]) ** 2\n",
+        "        if population > 1e-9:\n",
+        "            histogram[i] = population\n",
+        "    \n",
+        "    return histogram\n",
+        "\n",
+        "\n",
+        "def histogram_to_category(histogram):\n",
+        "    \"\"\"This function takes a histogram representation of circuit execution results, and processes into labels as described in\n",
+        "    the problem description.\"\"\"\n",
+        "    assert abs(sum(histogram.values())-1)<1e-8\n",
+        "    positive=0\n",
+        "    for key in histogram.keys():\n",
+        "        digits = bin(int(key))[2:].zfill(20)\n",
+        "        if digits[-1]=='0':\n",
+        "            positive+=histogram[key]\n",
+        "        \n",
+        "    return positive\n",
+        "\n",
+        "def count_gates(circuit: qiskit.QuantumCircuit) -> Dict[int, int]:\n",
+        "    \"\"\"Returns the number of gate operations with each number of qubits.\"\"\"\n",
+        "    counter = Counter([len(gate[1]) for gate in circuit.data])\n",
+        "    #feel free to comment out the following two lines. But make sure you don't have k-qubit gates in your circuit\n",
+        "    #for k>2\n",
+        "    for i in range(3,20):\n",
+        "        assert counter[i]==0\n",
+        "        \n",
+        "    return counter\n",
+        "\n",
+        "\n",
+        "def image_mse(image1,image2):\n",
+        "    # Using sklearns mean squared error:\n",
+        "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
+        "    return mean_squared_error(255*image1,255*image2)\n",
+        "\n",
+        "# [normalize]\n",
+        "def normalize(row):\n",
+        "    #We calculate the squareroot of the sum of the square values of the row\n",
+        "    normalization_factor = np.sqrt(np.sum(row**2)) \n",
+        "    if normalization_factor == 0.:\n",
+        "        #If the sum is zero we return a 0\n",
+        "        return 0.0\n",
+        "    #Else we divide each value between the sum value above\n",
+        "    row = row / normalization_factor\n",
+        "    return row, normalization_factor\n",
+        "#\n",
+        "data_path = \"./data\"\n",
+        "\n",
+        "def test():\n",
+        "    #load the actual hackthon data (fashion-mnist)\n",
+        "    images=np.load(data_path+'/images.npy')\n",
+        "    labels=np.load(data_path+'/labels.npy')\n",
+        "    \n",
+        "    #test part 1\n",
+        "    global pca, AMPLITUDE_ENCODING_N_QUBITS,IMAGE_SIZE, dataset\n",
+        "\n",
+        "    AMPLITUDE_ENCODING_N_QUBITS = 4\n",
+        "    IMAGE_SIZE = 28\n",
+        "    N_IMAGES = len(images)\n",
+        "\n",
+        "    pca = PCA(n_components=2**AMPLITUDE_ENCODING_N_QUBITS)\n",
+        "    \n",
+        "    data = images.reshape(N_IMAGES, IMAGE_SIZE * IMAGE_SIZE)\n",
+        "    pca.fit(data)\n",
+        "    \n",
+        "    # Apply dimensionality reduction on your data\n",
+        "    images_pca = pca.transform(data)\n",
+        "    \n",
+        "    dataset = []\n",
+        "    for i in tqdm.tqdm(range(len(images))):\n",
+        "        image_pca = images_pca[i]\n",
+        "        image_pca_min_ = image_pca.min()\n",
+        "        image_pca_positive = image_pca - image_pca_min_\n",
+        "        \n",
+        "        dataset_i = {}\n",
+        "        \n",
+        "        normalized_state, normalization_factor = normalize(image_pca_positive)\n",
+        "        \n",
+        "        dataset_i[\"image\"] = images[i]\n",
+        "        dataset_i[\"image_vector\"] = normalized_state\n",
+        "        dataset_i[\"label\"] = labels[i]\n",
+        "        dataset_i[\"normalization_factor\"] = normalization_factor\n",
+        "        dataset_i[\"pca_min_\"] = image_pca_min_\n",
+        "        \n",
+        "        dataset.append(dataset_i)\n",
+        "    \n",
+        "    n=len(images)\n",
+        "    mse=0\n",
+        "    gatecount=0\n",
+        "\n",
+        "    for i in tqdm.tqdm(range(n)):\n",
+        "        #encode image into circuit\n",
+        "        circuit,image_re=run_part1(dataset[i][\"image_vector\"])\n",
+        "        image_re = np.asarray(image_re)\n",
+        "\n",
+        "        #count the number of 2qubit gates used\n",
+        "        gatecount += count_gates(circuit)[2]\n",
+        "\n",
+        "        #calculate mse\n",
+        "        mse+=image_mse(dataset[i][\"image\"], image_re)\n",
+        "\n",
+        "    #fidelity of reconstruction\n",
+        "    f=1-mse/n\n",
+        "    gatecount=gatecount/n\n",
+        "\n",
+        "    #score for part1\n",
+        "    score_part1=f*(0.999**gatecount)\n",
+        "    \n",
+        "    print(score_part_1)\n",
+        "\n",
+        "    #test part 2\n",
+        "    \n",
+        "    score=0\n",
+        "    gatecount=0\n",
+        "    n=len(images)\n",
+        "\n",
+        "    for i in range(n):\n",
+        "        #run part 2\n",
+        "        circuit,label=run_part2(images[i])\n",
+        "\n",
+        "        #count the gate used in the circuit for score calculation\n",
+        "        gatecount+=count_gates(circuit)[2]\n",
+        "\n",
+        "        #check label\n",
+        "        if label==labels[i]:\n",
+        "            score+=1\n",
+        "    \n",
+        "    #score\n",
+        "    score=score/n\n",
+        "    gatecount=gatecount/n\n",
+        "\n",
+        "    score_part2=score*(0.999**gatecount)\n",
+        "    \n",
+        "    print(score_part1, \",\", score_part2, \",\", data_path, sep=\"\")\n",
+        "\n",
+        "\n",
+        "############################\n",
+        "#      YOUR CODE HERE      #\n",
+        "############################\n",
+        "def encode(image):\n",
+        "    qc = QuantumCircuit(AMPLITUDE_ENCODING_N_QUBITS)\n",
+        "\n",
+        "    qc.initialize(image)\n",
+        "    \n",
+        "    for i in range(AMPLITUDE_ENCODING_N_QUBITS + 2):\n",
+        "        qc = qc.decompose()\n",
+        "        \n",
+        "    return qc\n",
+        "\n",
+        "def decode(histogram):\n",
+        "    histogram_array = np.zeros(2 ** AMPLITUDE_ENCODING_N_QUBITS)\n",
+        "    for i in range(2 ** AMPLITUDE_ENCODING_N_QUBITS):\n",
+        "        histogram_array[i] = histogram.get(i, 0)\n",
+        "        \n",
+        "    root = np.sqrt(histogram_array)\n",
+        "    \n",
+        "    root = root * dataset[i][\"normalization_factor\"]\n",
+        "    \n",
+        "    root = root + dataset[i][\"pca_min_\"]\n",
+        "\n",
+        "    reconstruction = pca.inverse_transform([root])\n",
+        "\n",
+        "    \n",
+        "    image = reconstruction.reshape(IMAGE_SIZE, IMAGE_SIZE)\n",
+        "    \n",
+        "    return image\n",
+        "\n",
+        "def run_part1(image):\n",
+        "    #encode image into a circuit\n",
+        "    circuit=encode(image)\n",
+        "\n",
+        "    #simulate circuit\n",
+        "    histogram=simulate(circuit)\n",
+        "\n",
+        "    #reconstruct the image\n",
+        "    image_re=decode(histogram)\n",
+        "\n",
+        "    return circuit,image_re\n",
+        "\n",
+        "def run_part2(image):\n",
+        "    # load the quantum classifier circuit\n",
+        "    classifier=qiskit.QuantumCircuit.from_qasm_file('quantum_classifier.qasm')\n",
+        "    \n",
+        "    #encode image into circuit\n",
+        "    circuit=encode(image)\n",
+        "    \n",
+        "    #append with classifier circuit\n",
+        "    nq1 = circuit.width()\n",
+        "    nq2 = classifier.width()\n",
+        "    nq = max(nq1, nq2)\n",
+        "    qc = qiskit.QuantumCircuit(nq)\n",
+        "    qc.append(circuit.to_instruction(), list(range(nq1)))\n",
+        "    qc.append(classifier.to_instruction(), list(range(nq2)))\n",
+        "    \n",
+        "    #simulate circuit\n",
+        "    histogram=simulate(qc)\n",
+        "        \n",
+        "    #convert histogram to category\n",
+        "    label=histogram_to_category(histogram)\n",
+        "    \n",
+        "    #thresholding the label, any way you want\n",
+        "    if label>0.5:\n",
+        "        label=1\n",
+        "    else:\n",
+        "        label=0\n",
+        "        \n",
+        "    return circuit,label\n",
+        "\n",
+        "############################\n",
+        "#      END YOUR CODE       #\n",
+        "############################\n",
+        "\n",
+        "test()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.utils import shuffle\n",
+        "from sklearn.metrics import log_loss\n",
+        "import tqdm\n",
+        "from qiskit.circuit.library import RealAmplitudes\n",
+        "from qiskit.utils import algorithm_globals\n",
+        "\n",
+        "\n",
+        "from scipy.optimize import minimize \n",
+        "\n",
+        "\n",
+        "def ansatz(num_qubits):\n",
+        "    return RealAmplitudes(num_qubits, reps=5)\n",
+        "\n",
+        "#Function to identify a function cost\n",
+        "'''\n",
+        "Input: An array of parameters(vector of complex amplitudes)\n",
+        "Output: Function cost\n",
+        "'''\n",
+        "\n",
+        "def objective_function(params, images, labels, shots=1024, mini_batch=5, n_qubits=8):\n",
+        "    running_loss = 0\n",
+        "    images, labels = shuffle(images, labels)\n",
+        "    y_pred = []\n",
+        "\n",
+        "    #For each item of the trainig set\n",
+        "    for i in tqdm.tqdm(range(mini_batch)):\n",
+        "        \n",
+        "        image = images[i]\n",
+        "        \n",
+        "        circuit = encode(image) #Create a inicial circuit\n",
+        "        vqc = ansatz(n_qubits)\n",
+        "\n",
+        "        vqc = vqc.assign_parameters(params)\n",
+        "\n",
+        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
+        "        circuit_full = QuantumCircuit(n_qubits)\n",
+        "    \n",
+        "        qubits = list(range(n_qubits))\n",
+        "        \n",
+        "        circuit_full.append(circuit.to_instruction(), qubits)\n",
+        "        circuit_full.append(vqc.to_instruction(), qubits)\n",
+        "\n",
+        "        #Combine the initial circuit, the quantum variatinal circuit and the swap test\n",
+        "        #For the initial circuit and QVC we start at the qubit size_reduce + 1\n",
+        "        \n",
+        "        #Execute the circuit in the qasm_simulator\n",
+        "        histogram = simulate(circuit_full)\n",
+        "\n",
+        "        pred_label = histogram_to_category(histogram)\n",
+        "        \n",
+        "        y_pred.append(pred_label)\n",
+        "    \n",
+        "    print(labels.shape)\n",
+        "    \n",
+        "    print(len(y_pred))\n",
+        "    y_true = labels[:mini_batch].astype(int)\n",
+        "    \n",
+        "    \n",
+        "\n",
+        "    loss = log_loss(y_true, y_pred, labels=[0, 1])\n",
+        "    \n",
+        "    print(y_true, y_pred)\n",
+        "    print(\"Loss\")\n",
+        "    print(loss)\n",
+        "    \n",
+        "\n",
+        "    return loss\n",
+        "\n",
+        "def train_classifier():\n",
+        "  images=np.load(data_path+'/images.npy')\n",
+        "  labels=np.load(data_path+'/labels.npy')\n",
+        "\n",
+        "  N_images = len(images)    \n",
+        "  N_QUBITS = 4\n",
+        "  shots = 1024\n",
+        "  mini_batch = 25\n",
+        "\n",
+        "  vqc = ansatz(N_QUBITS)\n",
+        "  np.random.seed(42)\n",
+        "  initial_point = algorithm_globals.random.random(vqc.num_parameters)\n",
+        "\n",
+        "  #test part 1\n",
+        "  global pca, AMPLITUDE_ENCODING_N_QUBITS,IMAGE_SIZE, dataset\n",
+        "\n",
+        "  AMPLITUDE_ENCODING_N_QUBITS = 4\n",
+        "  IMAGE_SIZE = 28\n",
+        "  N_IMAGES = len(images)\n",
+        "\n",
+        "  pca = PCA(n_components=2**AMPLITUDE_ENCODING_N_QUBITS)\n",
+        "  \n",
+        "  data = images.reshape(N_IMAGES, IMAGE_SIZE * IMAGE_SIZE)\n",
+        "  pca.fit(data)\n",
+        "  \n",
+        "  # Apply dimensionality reduction on your data\n",
+        "  images_pca = pca.transform(data)\n",
+        "  # dataset pre-processing\n",
+        "  X_train = []\n",
+        "  dataset = []\n",
+        "  for i in tqdm.tqdm(range(len(images))):\n",
+        "      image_pca = images_pca[i]\n",
+        "      image_pca_min_ = image_pca.min()\n",
+        "      image_pca_positive = image_pca - image_pca_min_\n",
+        "      \n",
+        "      dataset_i = {}\n",
+        "      \n",
+        "      normalized_state, normalization_factor = normalize(image_pca_positive)\n",
+        "      \n",
+        "      dataset_i[\"image\"] = images[i]\n",
+        "      dataset_i[\"image_vector\"] = normalized_state\n",
+        "      dataset_i[\"label\"] = labels[i]\n",
+        "      dataset_i[\"normalization_factor\"] = normalization_factor\n",
+        "      dataset_i[\"pca_min_\"] = image_pca_min_\n",
+        "      X_train.append(normalized_state)\n",
+        "      dataset.append(dataset_i)\n",
+        "\n",
+        "  X_train = np.array(X_train)\n",
+        "\n",
+        "  #Minimization of the objective_fucntion by a COBYLA method\n",
+        "  minimum = minimize(objective_function, \n",
+        "                    initial_point, \n",
+        "                    args=(X_train, labels, shots, mini_batch, N_QUBITS), \n",
+        "                    method='COBYLA', \n",
+        "                    tol=1e-6,\n",
+        "                    options={\n",
+        "                        \"maxiter\": 100\n",
+        "                    }\n",
+        "                    )\n",
+        "  params = minimum.x #Get the solution array\n",
+        "  #Show the cost of the solution array\n",
+        "  #print(\"Cost: \", objective_function(params))\n",
+        "  print(params)\n",
+        "\n",
+        "  return params, minimum\n",
+        "\n",
+        "params, minimum = train_classifier()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kardiamJ16C-",
+        "outputId": "cbe18b21-af38-4fd7-f4ec-544e358d9164"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 2000/2000 [00:00<00:00, 24238.72it/s]\n",
+            "100%|██████████| 25/25 [00:05<00:00,  4.58it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0] [0.44519858402025986, 0.5085602502018445, 0.40632671451209795, 0.20092020244151756, 0.3977107001676153, 0.41472526492673534, 0.6251959425415752, 0.29942951568515996, 0.3723311667474298, 0.6656642171294723, 0.30642829538372324, 0.628821208180102, 0.40582039822943494, 0.6455649988743064, 0.39777949169730054, 0.3522800502215791, 0.6681438022149653, 0.27692736072648644, 0.7358593160250066, 0.4871031485313555, 0.7418107528346649, 0.7457709858003171, 0.40421499000484673, 0.6774790218773664, 0.7173770307390636]\n",
+            "Loss\n",
+            "0.6950484021617176\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.43it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 1 0 1 0 0 0 1 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0] [0.6713651596247816, 0.32797071264017913, 0.6159082805404793, 0.6948126275844074, 0.5181274856264813, 0.4416224649531305, 0.7651560098836326, 0.3891186175970222, 0.8428280495170285, 0.38185086463612594, 0.34402203331214204, 0.39887725924693174, 0.5719200121674068, 0.6428755510693174, 0.7002503570243181, 0.342133916074148, 0.41743510840906595, 0.6569694358548138, 0.7230374549782524, 0.5044971611725263, 0.9487756613093175, 0.4196608441677173, 0.38158536866452547, 0.5494787663284962, 0.81562557728394]\n",
+            "Loss\n",
+            "1.007307867649984\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.22it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0] [0.3161426804619409, 0.525812893918048, 0.15152449778814656, 0.5159805905465868, 0.30521341548078657, 0.627944887691082, 0.21770975675776247, 0.6530907982853387, 0.4014338771026449, 0.40717556762670043, 0.3896615528359388, 0.5639420091848597, 0.2775013762502942, 0.41409152496054935, 0.554807525668249, 0.36825999659599246, 0.5180061637894745, 0.6113942345112583, 0.6928522357788024, 0.5890127031885675, 0.28966828678925205, 0.588864736144661, 0.2270164169457504, 0.592547965790828, 0.3529251305449724]\n",
+            "Loss\n",
+            "0.5492886348847913\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.32it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0] [0.489827375402157, 0.36110212645926676, 0.23295179255985293, 0.2575735528362733, 0.30338182519741513, 0.2947606895479705, 0.218686257828648, 0.2625493709929567, 0.27985818747882485, 0.1779685396878382, 0.45939146186879665, 0.2954152989110424, 0.18178467127761633, 0.4042178128458602, 0.28523571013168103, 0.3046970339951046, 0.1938272322377514, 0.11179042638179577, 0.28760325075437876, 0.3596147639239648, 0.24603199478078613, 0.17891620899275845, 0.14200324937875072, 0.3574500805061423, 0.2379756713585529]\n",
+            "Loss\n",
+            "0.5299397602370532\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.32it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1] [0.1076630497237271, 0.3515095648675286, 0.3621367638714106, 0.055942117274697084, 0.5341235045371815, 0.5288924287427778, 0.39750470627839896, 0.36303646939700224, 0.15159662921810343, 0.4076748737811838, 0.20453772428573636, 0.3983483526914981, 0.4116847642750084, 0.5399922284087225, 0.38026968538055694, 0.461413773323828, 0.3635609378363328, 0.1498338322893529, 0.4665637784534496, 0.66313938010933, 0.3912011444603135, 0.2806752362687405, 0.11906109575875003, 0.35687829289123624, 0.3821415657587119]\n",
+            "Loss\n",
+            "0.5900131532286113\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:05<00:00,  4.92it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0] [0.6703963935419779, 0.5382387003989364, 0.5989349055525331, 0.49066693591667276, 0.6380069245212516, 0.8709332681278472, 0.652771723560037, 0.3310431373134298, 0.7184702211227347, 0.5679171370819375, 0.37921021544673966, 0.5485553716829865, 0.4436537452652735, 0.5579751232105111, 0.5482632606391545, 0.5171419535092207, 0.42849468302268334, 0.5661112304803004, 0.44672510833846857, 0.6327243230988808, 0.5512678404100027, 0.7624315155687088, 0.41069281281468917, 0.5403593632133151, 0.6182999935296536]\n",
+            "Loss\n",
+            "0.810766448882395\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.25it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [0.4058154382957029, 0.5121152048021309, 0.3595669412574303, 0.49173355580401584, 0.5048628073502891, 0.6769411534774596, 0.7934609608382284, 0.8114074698860186, 0.5352091120608897, 0.5940644898632671, 0.495142248055607, 0.7552141690717098, 0.7448197672155208, 0.6016208908224239, 0.8628053116607186, 0.42642623341605623, 0.514477190119382, 0.47256324327038257, 0.36005006886849045, 0.4230425962632963, 0.3928197725754425, 0.5173651819495484, 0.5109642019195214, 0.5351580036942589, 0.8232376277175146]\n",
+            "Loss\n",
+            "0.896769716361815\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.39it/s]\n"
+          ]
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+        {
+          "output_type": "stream",
+          "name": "stdout",
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+            "Loss\n",
+            "0.645266248037059\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.21it/s]\n"
+          ]
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+          "output_type": "stream",
+          "name": "stdout",
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+            "Loss\n",
+            "0.5939544107353314\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.40it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
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+            "(2000,)\n",
+            "25\n",
+            "[1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1] [0.06433534790970587, 0.5074828485062922, 0.16689421243740182, 0.15245690724781258, 0.13797539956913238, 0.3271524743972863, 0.09108654595203658, 0.6219615910961256, 0.1704373604772264, 0.0953124532373042, 0.19706159708424761, 0.2532514676238848, 0.5656205542821644, 0.38068193435224096, 0.3896439514201853, 0.6407888610594891, 0.09560933730969953, 0.16405764204831066, 0.09669101099151492, 0.5045810886907645, 0.2797203002864601, 0.10349913358846313, 0.42166852798793863, 0.3678460791475427, 0.22698816078398143]\n",
+            "Loss\n",
+            "0.9270658315828387\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.32it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
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+            "(2000,)\n",
+            "25\n",
+            "[0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1] [0.13915137442817482, 0.07208408427537984, 0.4722635006544194, 0.18562996310786123, 0.3468186788175827, 0.3269277267323909, 0.32580728509117124, 0.26450713805472714, 0.37545452503105387, 0.19414435318822404, 0.13541332814490334, 0.26541629323791943, 0.29239528933800607, 0.39266176713075773, 0.2612033251072293, 0.18060277433795305, 0.2641923265329735, 0.12845091354789995, 0.28334071469587546, 0.4083341238128736, 0.3448505812948566, 0.12017666332591435, 0.14135144742772707, 0.395483515856559, 0.2510788845001727]\n",
+            "Loss\n",
+            "0.5999810319327736\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:05<00:00,  4.75it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 0] [0.2972320755544233, 0.15535842241659148, 0.5543912365669875, 0.3301862850565475, 0.6492310260291216, 0.3071399967346729, 0.19897543077022337, 0.24684268540745924, 0.29507046140616094, 0.1438300999380153, 0.5090502575830858, 0.19191742849921176, 0.20735660143600076, 0.15144952677077164, 0.27859063600812756, 0.25171439818444746, 0.39927695310574524, 0.49131629280782163, 0.6525807120455412, 0.35171265355034753, 0.21483533943135782, 0.5308745958899868, 0.2737170991012762, 0.14056943722756485, 0.22087953218398573]\n",
+            "Loss\n",
+            "0.6555997670349555\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:05<00:00,  4.49it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1] [0.4962013147404283, 0.23515931151877342, 0.5377658549385856, 0.22765502377354838, 0.38857728095276023, 0.17841334087006577, 0.31955390185775095, 0.1780872123640531, 0.18257374373016708, 0.3220702775793027, 0.5240882178897629, 0.16426217496200202, 0.3369766549144346, 0.2671299806820289, 0.2277529216364139, 0.5800454614336916, 0.3910992727185255, 0.32812093409596427, 0.28371424868748574, 0.15663868270246806, 0.09988518523427102, 0.2247510290204852, 0.3095220873680078, 0.35766927924880704, 0.21670545169387886]\n",
+            "Loss\n",
+            "0.7439377488351986\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.30it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2000,)\n",
+            "25\n",
+            "[1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0] [0.6375373115932557, 0.394921550933168, 0.6375851358021226, 0.48039638807550555, 0.5323296170497155, 0.5979325479190568, 0.5792895371823035, 0.5881683253618746, 0.4975070032652983, 0.8306105971570923, 0.46923427884311003, 0.5204673237099549, 0.5506655155695737, 0.6294536642762751, 0.6715040891497774, 0.6445704930424091, 0.6564763573614711, 0.6329023964831019, 0.44665351258284325, 0.49039718126039245, 0.7556471632490424, 0.5724830537097136, 0.5632650061496771, 0.514590274271011, 0.6121567859133411]\n",
+            "Loss\n",
+            "0.8051027275410554\n"
+          ]
+        },
+        {
+          "output_type": "stream",
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+            "Loss\n",
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+            "Loss\n",
+            "0.5743672965309764\n"
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+            "100%|██████████| 25/25 [00:04<00:00,  5.44it/s]\n"
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+            "Loss\n",
+            "0.532458449969321\n"
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+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 25/25 [00:04<00:00,  5.29it/s]"
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+        {
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+            "25\n",
+            "[0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0] [0.3417753386555086, 0.5536320468722367, 0.42613222915669113, 0.4412413804602271, 0.3205896641985686, 0.42071812688023735, 0.5023080336617323, 0.4804877689496918, 0.37041189940560204, 0.5967165922866011, 0.468403262165466, 0.3624440501946382, 0.35831932881784306, 0.4905533743320733, 0.3694065635938489, 0.45968188883127525, 0.46445083378450114, 0.28430256309503754, 0.23795509623995448, 0.22151486007673432, 0.2759404667112512, 0.22965115146463683, 0.7141930492003385, 0.4768950328074967, 0.2394397339014594]\n",
+            "Loss\n",
+            "0.5776595803693259\n",
+            "[ 0.55562182  1.14319133  1.0737959   0.40937078  0.0165973   0.50865298\n",
+            "  0.30024442  0.1364341  -0.06497169  0.57702616  0.13664098  1.24139061\n",
+            "  0.45566212  0.64294409  0.57014194  0.03931625  0.87284308  0.49789603\n",
+            "  0.79267549  0.03023329  0.44871798  1.52403189  0.8777964   0.5086433 ]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_params = params"
+      ],
+      "metadata": {
+        "id": "9mKYCp4u7g9p"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vqc."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "id": "QhCDvNUq9NMD",
+        "outputId": "e6da79f8-5307-48e7-e1d4-0bbf58dcef6f"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     »\n",
+              "q_0: »\n",
+              "     »\n",
+              "q_1: »\n",
+              "     »\n",
+              "q_2: »\n",
+              "     »\n",
+              "q_3: »\n",
+              "     »\n",
+              "«     ┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
+              "«q_0: ┤0                                                                                                                                                       ├\n",
+              "«     │                                                                                                                                                        │\n",
+              "«q_1: ┤1                                                                                                                                                       ├\n",
+              "«     │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7],θ[8],θ[9],θ[10],θ[11],θ[12],θ[13],θ[14],θ[15],θ[16],θ[17],θ[18],θ[19],θ[20],θ[21],θ[22],θ[23]) │\n",
+              "«q_2: ┤2                                                                                                                                                       ├\n",
+              "«     │                                                                                                                                                        │\n",
+              "«q_3: ┤3                                                                                                                                                       ├\n",
+              "«     └────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘"
+            ],
+            "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: »\n",
+              "     »\n",
+              "q_1: »\n",
+              "     »\n",
+              "q_2: »\n",
+              "     »\n",
+              "q_3: »\n",
+              "     »\n",
+              "«     ┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
+              "«q_0: ┤0                                                                                                                                                       ├\n",
+              "«     │                                                                                                                                                        │\n",
+              "«q_1: ┤1                                                                                                                                                       ├\n",
+              "«     │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7],θ[8],θ[9],θ[10],θ[11],θ[12],θ[13],θ[14],θ[15],θ[16],θ[17],θ[18],θ[19],θ[20],θ[21],θ[22],θ[23]) │\n",
+              "«q_2: ┤2                                                                                                                                                       ├\n",
+              "«     │                                                                                                                                                        │\n",
+              "«q_3: ┤3                                                                                                                                                       ├\n",
+              "«     └────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from qiskit import QuantumCircuit\n",
+        "N_QUBITS = 4\n",
+        "vqc = ansatz(N_QUBITS)\n",
+        "\n",
+        "vqc = vqc.assign_parameters(best_params)\n",
+        "\n",
+        "vqc.qasm(filename=\"quantum_classifier.qasm\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 268
+        },
+        "id": "gfq8UhJH7CEn",
+        "outputId": "556e5a7c-ec1e-4da6-f764-62130a5ce4c0"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\ngate gate_RealAmplitudes(param0,param1,param2,param3,param4,param5,param6,param7,param8,param9,param10,param11,param12,param13,param14,param15,param16,param17,param18,param19,param20,param21,param22,param23) q0,q1,q2,q3 { ry(0.555621819228932) q0; ry(1.14319132801287) q1; ry(1.07379590145389) q2; ry(0.409370781763752) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.0165973010020706) q0; ry(0.508652978720279) q1; ry(0.300244419920286) q2; ry(0.136434104028988) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(-0.0649716942665253) q0; ry(0.577026156276957) q1; ry(0.13664098460932) q2; ry(1.24139060584453) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.455662122094092) q0; ry(0.642944089110158) q1; ry(0.570141938819558) q2; ry(0.0393162465417179) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.872843078509701) q0; ry(0.497896034397818) q1; ry(0.792675492672247) q2; ry(0.0302332873841969) q3; cx q2,q3; cx q1,q2; cx q0,q1; ry(0.448717981940666) q0; ry(1.52403189005694) q1; ry(0.877796397476269) q2; ry(0.508643300858681) q3; }\\nqreg q[4];\\ngate_RealAmplitudes(0.555621819228932,1.14319132801287,1.07379590145389,0.409370781763752,0.0165973010020706,0.508652978720279,0.300244419920286,0.136434104028988,-0.0649716942665253,0.577026156276957,0.13664098460932,1.24139060584453,0.455662122094092,0.642944089110158,0.570141938819558,0.0393162465417179,0.872843078509701,0.497896034397818,0.792675492672247,0.0302332873841969,0.448717981940666,1.52403189005694,0.877796397476269,0.508643300858681) q[0],q[1],q[2],q[3];\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a7ArfSyV9Xiq",
+        "outputId": "f06143d5-aaf0-411e-81a9-079f79e989b0"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "data  quantum_classifier.qasm  sample_data\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "files.download('quantum_classifier.qasm')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "XZTU5w3x9cel",
+        "outputId": "6994ed13-9732-4aea-88b0-c04ddf7f33bb"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_4991390e-5a53-4b29-ab15-96165d0de025\", \"quantum_classifier.qasm\", 1519)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vqc = ansatz(N_QUBITS)"
+      ],
+      "metadata": {
+        "id": "k2MPHTTD7lmK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "params"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "id": "6GvYf_fP2TG_",
+        "outputId": "b9f0011e-dac5-448b-e933-a58af1de4db4"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-17-bfd368200e6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'params' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "DVtOX_Ep6ve8"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file