Encoders

run_part1.py

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a8f1303-c991-4af4-9cad-649ae271c00f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import qiskit\n",
+    "\n",
+    "# Encoder function to convert an image into a quantum circuit\n",
+    "def encoder(image):\n",
+    "    height, width = image.shape\n",
+    "    print(image.shape)\n",
+    "    qc = qiskit.QuantumCircuit(height * width, height * width)\n",
+    "\n",
+    "    # Apply Hadamard gates to the qubits\n",
+    "    for i in range(height * width):\n",
+    "        qc.h(i)\n",
+    "\n",
+    "    # Apply the NEQR encoding\n",
+    "    for row in range(height):\n",
+    "        for col in range(width):\n",
+    "            # Get the pixel value at the current position\n",
+    "            pixel_value = image[row][col]\n",
+    "\n",
+    "            # Apply controlled-Z gates based on the pixel value\n",
+    "            for i in range(height * width):\n",
+    "                if (i // width == row and i % width == col):\n",
+    "                    if pixel_value == 0:\n",
+    "                        qc.z(i)\n",
+    "                else:\n",
+    "                    qc.cz(i, (row * width) + col)\n",
+    "\n",
+    "    return qc\n",
+    "\n",
+    "# Simulator function to simulate a circuit and get a histogram\n",
+    "def simulator(circuit):\n",
+    "    # Simulate the circuit using the statevector simulator\n",
+    "    backend = qiskit.Aer.get_backend('statevector_simulator')\n",
+    "    result = qiskit.execute(circuit, backend).result()\n",
+    "    statevector = result.get_statevector()\n",
+    "\n",
+    "    # Get the probability distribution of the statevector\n",
+    "    probabilities = np.abs(statevector)**2\n",
+    "\n",
+    "    return probabilities\n",
+    "\n",
+    "# Decoder function to convert the histogram into a regenerated image\n",
+    "def decoder(histogram, image):\n",
+    "    # Reshape the histogram into an image\n",
+    "    height, width = image.shape\n",
+    "    image = np.zeros((height, width))\n",
+    "\n",
+    "    for i in range(height * width):\n",
+    "        row = i // width\n",
+    "        col = i % width\n",
+    "        image[row][col] = histogram[i]\n",
+    "\n",
+    "    return image\n",
+    "\n",
+    "# Example usage\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Load the image\n",
+    "    data_path='data'\n",
+    "#load the actual hackthon data (fashion-mnist)\n",
+    "    image=np.load(data_path+'/images.npy')\n",
+    "    #image = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])\n",
+    "\n",
+    "    # Convert the image into a quantum circuit\n",
+    "    circuit = encoder(image[1])\n",
+    "\n",
+    "    # Simulate the circuit and get a histogram\n",
+    "    histogram = simulator(circuit)\n",
+    "\n",
+    "    # Convert the histogram into a regenerated image\n",
+    "    regenerated_image = decoder(histogram, image[1])\n",
+    "\n",
+    "    print(\"Original Image:\")\n",
+    "    print(image)\n",
+    "    print(histogram)\n",
+    "    print(\"Regenerated Image:\")\n",
+    "    print(regenerated_image)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5acf433-e1d7-4a07-81c7-355ef73c7aaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(image[1])\n",
+    "plt.imshow(regenerated_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a100e8c4-d54d-498d-ae02-9b17636cecbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 [IonQ]",
+   "language": "python",
+   "name": "python3_ionq_6vdluz"
+  },
+  "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
+}

run_part2.py

@@ -0,0 +1,88 @@
+def encode(image):
+    q = qiskit.QuantumRegister(3)
+    qc = qiskit.QuantumCircuit(q)
+    img = images
+    image = np.array(images)
+    if image[0][0]==0:
+        circuit.rx(np.pi,0)
+    else:
+        theta = np.pi #  according to line circuit.rx(np.pi,0)
+        qc.h(0)
+        qc.h(1)
+
+        qc.barrier()
+
+
+        qc.cry(theta,0,2)
+        qc.cx(0,1)
+        qc.cry(-theta,1,2)
+        qc.cx(0,1)
+        qc.cry(theta,1,2)
+
+        qc.barrier()
+
+
+        qc.x(1)
+        qc.cry(theta,0,2)
+        qc.cx(0,1)
+        qc.cry(-theta,1,2)
+        qc.cx(0,1)
+        qc.cry(theta,1,2)
+
+        qc.barrier()
+
+        qc.x(1)
+        qc.x(0)
+        qc.cry(theta,0,2)
+        qc.cx(0,1)
+        qc.cry(-theta,1,2)
+        qc.cx(0,1)
+        qc.cry(theta,1,2)
+
+
+        qc.barrier()
+
+        qc.x(1)
+
+        qc.cry(theta,0,2)
+        qc.cx(0,1)
+        qc.cry(-theta,1,2)
+        qc.cx(0,1)
+        qc.cry(theta,1,2)
+
+        qc.measure_all()
+
+        qc.draw()
+    return qc
+def decode(histogram):
+    if 1 in histogram.keys():
+        image=np.array([[0,0],[0,0]])
+    else:
+        image=np.array([[1,1],[1,1]])
+    return image
+
+
+def run_part2(image):
+
+    #loade the quantum classifier circuit
+    classifier=qiskit.QuantumCircuit.from_qasm_file('quantum_classifier.qasm')
+
+    #encode image into circuit
+    qc=encode(image)
+
+    #append with classifier circuit
+    nq1 = qc.width()
+    nq2 = classifier.width()
+    nq = max(nq1, nq2)
+    qc = qiskit.QuantumCircuit(nq)
+    qc.append(qc.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)
+
+    return qc,label
+#score