523 lines (523 with data), 198.0 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "d265704a-9f2a-4e68-ece8-fb75cf761c67"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696822902.3670027\n",
"Mon Oct 9 03:41:42 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": 62,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "f6e69f24-93f2-4dfd-ac60-311435e67d84"
},
"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": 63,
"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": 64,
"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": 65,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "16b3f353-ec5e-45d1-b882-01c293bc4a2a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.422173 | Train accuracy: 0.495000 | Test Accuracy: 0.443500\n",
"Epoch: 1 | Loss: 0.319732 | Train accuracy: 0.500000 | Test accuracy: 0.452500\n",
"Epoch: 2 | Loss: 0.201751 | Train accuracy: 0.690000 | Test accuracy: 0.645000\n",
"Epoch: 3 | Loss: 0.205687 | Train accuracy: 0.675000 | Test accuracy: 0.637500\n",
"Epoch: 4 | Loss: 0.237900 | Train accuracy: 0.630000 | Test accuracy: 0.645000\n",
"Epoch: 5 | Loss: 0.126498 | Train accuracy: 0.895000 | Test accuracy: 0.867000\n",
"Epoch: 6 | Loss: 0.253724 | Train accuracy: 0.570000 | Test accuracy: 0.599000\n",
"Epoch: 7 | Loss: 0.155788 | Train accuracy: 0.760000 | Test accuracy: 0.781000\n",
"Epoch: 8 | Loss: 0.120898 | Train accuracy: 0.850000 | Test accuracy: 0.812500\n",
"Epoch: 9 | Loss: 0.248873 | Train accuracy: 0.590000 | Test accuracy: 0.533500\n",
"Epoch: 10 | Loss: 0.115013 | Train accuracy: 0.850000 | Test accuracy: 0.793000\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 = 6\n",
"learning_rate = 0.6\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": 66,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 454
},
"id": "ZPszGYA3Tnyy",
"outputId": "410c29a2-13f7-405a-d435-037ab2161385"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.115013 | Train accuracy 0.850000 | Test Accuracy : 0.793000\n",
"Learned weights\n",
"Layer 0: [-1.32706321 0.06473101 0.04727113]\n",
"Layer 1: [-0.69847904 -1.67785535 2.43386915]\n",
"Layer 2: [2.92426867 1.24587418 4.99373694]\n",
"Layer 3: [ 2.07235568 4.6672727 -0.44971301]\n",
"Layer 4: [ 0.39449211 5.27651753 -0.37972196]\n",
"Layer 5: [-0.59551443 0.28818483 0.31563322]\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": "b86c87a5-5fb8-41fa-d41a-7d52c57b01bf"
},
"execution_count": 67,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696823377.5250344\n",
"Mon Oct 9 03:49:37 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
}