ionQ Hackhaton

part1.ipynb

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+
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd6bee73-2f0f-427c-afa8-c14089574323",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# part 1\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"
+   ]
+  }
+ ],
+ "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
+}

part1.py

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+# Adnaan Chida
+# [email protected]
+
+# part 1
+
+n=len(dataset)
+mse=0
+gatecount=0
+
+# Functions 'encode' and 'decode' are dummy.
+def encode(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
+
+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

part2.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "741b396e-1c49-4f23-a46d-fa47f4ca2f46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# part 2\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",
+    "    #load 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"
+   ]
+  }
+ ],
+ "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
+}

part2.py

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+# Adnaan Chida
+# [email protected]
+
+# part 2
+
+def encode(image):
+    circuit=cirq.Circuit()
+    if image[0][0]==0:
+        circuit.append(cirq.rx(np.pi).on(cirq.LineQubit(0)))
+    return circuit
+
+
+def run_part2(image):
+
+    #load the quantum classifier circuit
+    with open('part2.pickle', 'rb') as f:
+        classifier=pickle.load(f)
+
+    #encode image into circuit
+    circuit=encode(image)
+
+    #append with classifier circuit
+
+    circuit.append(classifier)
+
+    #simulate circuit
+    histogram=simulate(circuit)
+
+    #convert histogram to category
+    label=histogram_to_category(histogram)
+
+    return circuit
+    return label