520 lines (520 with data), 196.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "c46ddc7d-0f23-48c5-bef9-9b3c358875c1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696869114.097024\n",
"Mon Oct 9 16:31:54 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": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "ad6157a6-a142-408a-dc6a-1f99f93a1108"
},
"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": 32,
"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.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": 33,
"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": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "43b44ba0-6390-4d60-b4c3-979280af96c0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
"Epoch: 1 | Loss: 0.178247 | Train accuracy: 0.720000 | Test accuracy: 0.655000\n",
"Epoch: 2 | Loss: 0.160878 | Train accuracy: 0.785000 | Test accuracy: 0.759000\n",
"Epoch: 3 | Loss: 0.163896 | Train accuracy: 0.775000 | Test accuracy: 0.759500\n",
"Epoch: 4 | Loss: 0.166055 | Train accuracy: 0.755000 | Test accuracy: 0.726000\n",
"Epoch: 5 | Loss: 0.159972 | Train accuracy: 0.775000 | Test accuracy: 0.759500\n",
"Epoch: 6 | Loss: 0.136857 | Train accuracy: 0.795000 | Test accuracy: 0.777500\n",
"Epoch: 7 | Loss: 0.116859 | Train accuracy: 0.845000 | Test accuracy: 0.811000\n",
"Epoch: 8 | Loss: 0.121193 | Train accuracy: 0.820000 | Test accuracy: 0.814500\n",
"Epoch: 9 | Loss: 0.122907 | Train accuracy: 0.825000 | Test accuracy: 0.813500\n",
"Epoch: 10 | Loss: 0.107399 | Train accuracy: 0.885000 | Test accuracy: 0.848000\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 = 0.6\n",
"epochs = 10\n",
"batch_size = 28\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": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"id": "ZPszGYA3Tnyy",
"outputId": "9cec5cba-b718-4719-b4df-8134dd6ab847"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.107399 | Train accuracy 0.885000 | Test Accuracy : 0.848000\n",
"Learned weights\n",
"Layer 0: [-0.6301876 1.28418923 0.02583686]\n",
"Layer 1: [0.53008209 0.76303212 0.1443008 ]\n",
"Layer 2: [-1.01047189 1.12114504 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": "b95a6589-7da4-4ecc-9bac-374d374022eb"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696869390.6950307\n",
"Mon Oct 9 16:36:30 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
}