521 lines (521 with data), 195.5 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "8622bbc7-e40e-4426-cea9-31bd383906b7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696834110.5646367\n",
"Mon Oct 9 06:48:30 2023\n"
]
}
],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"%matplotlib inline\n",
"# !pip install pennylane\n",
"\n",
"import time\n",
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "1e60d8f6-a94b-49a2-ea37-081cec25362e"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import pennylane as qml\n",
"from pennylane import numpy as np\n",
"from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# Set a random seed\n",
"np.random.seed(42)\n",
"\n",
"\n",
"# Make a dataset of points inside and outside of a circle\n",
"def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
" \"\"\"\n",
" Generates a dataset of points with 1/0 labels inside a given radius.\n",
"\n",
" Args:\n",
" samples (int): number of samples to generate\n",
" center (tuple): center of the circle\n",
" radius (float: radius of the circle\n",
"\n",
" Returns:\n",
" Xvals (array[tuple]): coordinates of points\n",
" yvals (array[int]): classification labels\n",
" \"\"\"\n",
" Xvals, yvals = [], []\n",
"\n",
" for i in range(samples):\n",
" x = 2 * (np.random.rand(2)) - 1\n",
" y = 0\n",
" if np.linalg.norm(x - center) < radius:\n",
" y = 1\n",
" Xvals.append(x)\n",
" yvals.append(y)\n",
" return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
"\n",
"\n",
"def plot_data(x, y, fig=None, ax=None):\n",
" \"\"\"\n",
" Plot data with red/blue values for a binary classification.\n",
"\n",
" Args:\n",
" x (array[tuple]): array of data points as tuples\n",
" y (array[int]): array of data points as tuples\n",
" \"\"\"\n",
" if fig == None:\n",
" fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
" reds = y == 0\n",
" blues = y == 1\n",
" ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
" ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
" ax.set_xlabel(\"$x_1$\")\n",
" ax.set_ylabel(\"$x_2$\")\n",
"\n",
"\n",
"Xdata, ydata = circle(500)\n",
"fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
"plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
"plt.show()\n",
"\n",
"\n",
"# Define output labels as quantum state vectors\n",
"def density_matrix(state):\n",
" \"\"\"Calculates the density matrix representation of a state.\n",
"\n",
" Args:\n",
" state (array[complex]): array representing a quantum state vector\n",
"\n",
" Returns:\n",
" dm: (array[complex]): array representing the density matrix\n",
" \"\"\"\n",
" return state * np.conj(state).T\n",
"\n",
"\n",
"label_0 = [[1], [0]]\n",
"label_1 = [[0], [1]]\n",
"state_labels = np.array([label_0, label_1], requires_grad=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqAg65FbTnyx"
},
"source": [
"Simple classifier with data reloading and fidelity loss\n",
"=======================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"id": "ZyKIWD9bTnyx"
},
"outputs": [],
"source": [
"dev = qml.device(\"lightning.qubit\", wires=1)\n",
"# Install any pennylane-plugin to run on some particular backend\n",
"\n",
"\n",
"@qml.qnode(dev, interface=\"autograd\")\n",
"def qcircuit(params, x, y):\n",
" \"\"\"A variational quantum circuit representing the Universal classifier.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): single input vector\n",
" y (array[float]): single output state density matrix\n",
"\n",
" Returns:\n",
" float: fidelity between output state and input\n",
" \"\"\"\n",
" for p in params:\n",
" qml.PauliZ(wires= 0)\n",
" qml.Rot(*x, wires=0)\n",
" qml.Rot(*p, wires=0)\n",
" return qml.expval(qml.Hermitian(y, wires=[0]))\n",
"\n",
"\n",
"def cost(params, x, y, state_labels=None):\n",
" \"\"\"Cost function to be minimized.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): array of state representations for labels\n",
"\n",
" Returns:\n",
" float: loss value to be minimized\n",
" \"\"\"\n",
" # Compute prediction for each input in data batch\n",
" loss = 0.0\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" for i in range(len(x)):\n",
" f = qcircuit(params, x[i], dm_labels[y[i]])\n",
" loss = loss + (1 - f) ** 2\n",
" return loss / len(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Bz83H0xTnyx"
},
"source": [
"Utility functions for testing and creating batches\n",
"==================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"id": "i1-TLseuTnyx"
},
"outputs": [],
"source": [
"def test(params, x, y, state_labels=None):\n",
" \"\"\"\n",
" Tests on a given set of data.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" predicted (array([int]): predicted labels for test data\n",
" output_states (array[float]): output quantum states from the circuit\n",
" \"\"\"\n",
" fidelity_values = []\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" predicted = []\n",
"\n",
" for i in range(len(x)):\n",
" fidel_function = lambda y: qcircuit(params, x[i], y)\n",
" fidelities = [fidel_function(dm) for dm in dm_labels]\n",
" best_fidel = np.argmax(fidelities)\n",
"\n",
" predicted.append(best_fidel)\n",
" fidelity_values.append(fidelities)\n",
"\n",
" return np.array(predicted), np.array(fidelity_values)\n",
"\n",
"\n",
"def accuracy_score(y_true, y_pred):\n",
" \"\"\"Accuracy score.\n",
"\n",
" Args:\n",
" y_true (array[float]): 1-d array of targets\n",
" y_predicted (array[float]): 1-d array of predictions\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" score (float): the fraction of correctly classified samples\n",
" \"\"\"\n",
" score = y_true == y_pred\n",
" return score.sum() / len(y_true)\n",
"\n",
"\n",
"def iterate_minibatches(inputs, targets, batch_size):\n",
" \"\"\"\n",
" A generator for batches of the input data\n",
"\n",
" Args:\n",
" inputs (array[float]): input data\n",
" targets (array[float]): targets\n",
"\n",
" Returns:\n",
" inputs (array[float]): one batch of input data of length `batch_size`\n",
" targets (array[float]): one batch of targets of length `batch_size`\n",
" \"\"\"\n",
" for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
" idxs = slice(start_idx, start_idx + batch_size)\n",
" yield inputs[idxs], targets[idxs]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B5s1nRL2Tnyy"
},
"source": [
"Train a quantum classifier on the circle dataset\n",
"================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "f153d983-4dc9-4a08-db25-044ac0f5d9d9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.443627 | Train accuracy: 0.530000 | Test Accuracy: 0.474000\n",
"Epoch: 1 | Loss: 0.266548 | Train accuracy: 0.600000 | Test accuracy: 0.579000\n",
"Epoch: 2 | Loss: 0.215893 | Train accuracy: 0.690000 | Test accuracy: 0.641500\n",
"Epoch: 3 | Loss: 0.203104 | Train accuracy: 0.690000 | Test accuracy: 0.667000\n",
"Epoch: 4 | Loss: 0.214468 | Train accuracy: 0.685000 | Test accuracy: 0.685000\n",
"Epoch: 5 | Loss: 0.199155 | Train accuracy: 0.690000 | Test accuracy: 0.644000\n",
"Epoch: 6 | Loss: 0.204857 | Train accuracy: 0.700000 | Test accuracy: 0.714500\n",
"Epoch: 7 | Loss: 0.189675 | Train accuracy: 0.720000 | Test accuracy: 0.720500\n",
"Epoch: 8 | Loss: 0.183334 | Train accuracy: 0.735000 | Test accuracy: 0.709000\n",
"Epoch: 9 | Loss: 0.186856 | Train accuracy: 0.755000 | Test accuracy: 0.772500\n",
"Epoch: 10 | Loss: 0.152825 | Train accuracy: 0.820000 | Test accuracy: 0.813000\n"
]
}
],
"source": [
"# Generate training and test data\n",
"num_training = 200\n",
"num_test = 2000\n",
"\n",
"Xdata, y_train = circle(num_training)\n",
"X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
"\n",
"Xtest, y_test = circle(num_test)\n",
"X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
"\n",
"\n",
"# Train using Adam optimizer and evaluate the classifier\n",
"num_layers = 3\n",
"learning_rate = 0.62\n",
"epochs = 10\n",
"batch_size = 32\n",
"\n",
"opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
"\n",
"# initialize random weights\n",
"params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
"\n",
"predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
"accuracy_train = accuracy_score(y_train, predicted_train)\n",
"\n",
"predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
"accuracy_test = accuracy_score(y_test, predicted_test)\n",
"\n",
"# save predictions with random weights for comparison\n",
"initial_predictions = predicted_test\n",
"\n",
"loss = cost(params, X_test, y_test, state_labels)\n",
"\n",
"print(\n",
" \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
" 0, loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"for it in range(epochs):\n",
" for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
" params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
"\n",
" predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
" accuracy_train = accuracy_score(y_train, predicted_train)\n",
" loss = cost(params, X_train, y_train, state_labels)\n",
"\n",
" predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
" accuracy_test = accuracy_score(y_test, predicted_test)\n",
" res = [it + 1, loss, accuracy_train, accuracy_test]\n",
" print(\n",
" \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
" *res\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YWspC-9BTnyy"
},
"source": [
"Results\n",
"=======\n"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"id": "ZPszGYA3Tnyy",
"outputId": "f16dea3b-7b89-49a7-9bc6-15eb7cd9dd56"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.152825 | Train accuracy 0.820000 | Test Accuracy : 0.813000\n",
"Learned weights\n",
"Layer 0: [1.01265485 4.41376705 3.36445664]\n",
"Layer 1: [-1.24668947 -1.42280209 -2.63190172]\n",
"Layer 2: [-2.28244287 0.69617059 0.32099705]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"print(\n",
" \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
" loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"print(\"Learned weights\")\n",
"for i in range(num_layers):\n",
" print(\"Layer {}: {}\".format(i, params[i]))\n",
"\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
"plot_data(X_test, initial_predictions, fig, axes[0])\n",
"plot_data(X_test, predicted_test, fig, axes[1])\n",
"plot_data(X_test, y_test, fig, axes[2])\n",
"axes[0].set_title(\"Predictions with random weights\")\n",
"axes[1].set_title(\"Predictions after training\")\n",
"axes[2].set_title(\"True test data\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sYQWz6IdTnyy"
},
"source": [
"References\n",
"==========\n",
"\n",
"\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
"quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
"\n",
"\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
"optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
"\n",
"\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
"for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
"503-528.\n",
"\n",
"About the author\n",
"================\n"
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "8FGVwF_QUYza",
"outputId": "62ff41c5-da28-49b1-9213-9f964601113c"
},
"execution_count": 198,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696834395.6517417\n",
"Mon Oct 9 06:53:15 2023\n"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.18"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 0
}