Feynman Prodigies Submission IonQ

IonQ Challenge/mock_data_creator.ipynb

@@ -0,0 +1,79 @@
+{
+ "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

IonQ Challenge/part1_example_qiskit.ipynb

@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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(255*image1,255*image2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x126d3ebe0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mWVNVc36BclLe/mwFNnfLm4EvzNwhyeokK7vlNcBZwO6RTTh/8/lqQv98Lwa2V3flbAk77HnNuM6wEfjWCOc7krYCb+s+BXol8Hjv7frEmuPrNXMb9xXoeV6lPh64HdgD3AYc163fAFzbLb8a2MXgU4ddwKXjnnuO87kAuJ/Bq6gru3VXARu75VXAZ4G9wNeAU8c9c6Pz+gvg3u7f6EvAi8Y98zzP6wbgYeAnDK6XXAq8A3hHtz0M/tjYA93/vQ3jnrnReV3e+/faAbx6Ps/rr+lLampS3v5ImhCGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdTU/wMK0Y3svb+MHgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_path='mock_data'\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",
+    "#you can visualize it\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(images[1])"
+   ]
+  },
+  {
+   "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(images)\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(images)\n",
+    "mse=0\n",
+    "gatecount=0\n",
+    "\n",
+    "for data in images:\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\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 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}