527 lines (527 with data), 201.7 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "4899b578-e2e4-4d52-8603-a304a915a4c9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696826968.6558547\n",
"Mon Oct 9 04:49:28 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": 90,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "37618621-70ab-4350-9561-dfe96f507a2a"
},
"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": 91,
"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": 92,
"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": 93,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "41dadaba-fdf7-4167-befc-590e2ebf213c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.405317 | Train accuracy: 0.320000 | Test Accuracy: 0.323500\n",
"Epoch: 1 | Loss: 0.212125 | Train accuracy: 0.655000 | Test accuracy: 0.590000\n",
"Epoch: 2 | Loss: 0.166624 | Train accuracy: 0.755000 | Test accuracy: 0.727500\n",
"Epoch: 3 | Loss: 0.297095 | Train accuracy: 0.570000 | Test accuracy: 0.504000\n",
"Epoch: 4 | Loss: 0.235329 | Train accuracy: 0.625000 | Test accuracy: 0.616000\n",
"Epoch: 5 | Loss: 0.218562 | Train accuracy: 0.620000 | Test accuracy: 0.655000\n",
"Epoch: 6 | Loss: 0.180546 | Train accuracy: 0.780000 | Test accuracy: 0.743500\n",
"Epoch: 7 | Loss: 0.112727 | Train accuracy: 0.895000 | Test accuracy: 0.840000\n",
"Epoch: 8 | Loss: 0.142111 | Train accuracy: 0.805000 | Test accuracy: 0.757500\n",
"Epoch: 9 | Loss: 0.138826 | Train accuracy: 0.800000 | Test accuracy: 0.778500\n",
"Epoch: 10 | Loss: 0.179910 | Train accuracy: 0.690000 | Test accuracy: 0.671500\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 = 10\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": 94,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 527
},
"id": "ZPszGYA3Tnyy",
"outputId": "e1359cc9-17ec-4b22-d13b-00f67891494d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.179910 | Train accuracy 0.690000 | Test Accuracy : 0.671500\n",
"Learned weights\n",
"Layer 0: [-1.68937657 -0.90771473 5.7755439 ]\n",
"Layer 1: [6.32290578 0.78670321 1.00993827]\n",
"Layer 2: [ 0.11603089 -1.41395018 1.60621505]\n",
"Layer 3: [ 0.06739231 3.50267185 -0.22347799]\n",
"Layer 4: [-1.16245364 0.23135108 1.20124368]\n",
"Layer 5: [ 1.35897004 -0.69391377 0.53894822]\n",
"Layer 6: [2.93612691 0.13190853 1.50901864]\n",
"Layer 7: [-0.35255005 4.4899993 -1.46457383]\n",
"Layer 8: [-1.44859349 4.01263116 1.01431184]\n",
"Layer 9: [-0.19666319 -3.72710042 0.71598469]\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": "a818eac0-cfe2-482e-ee0b-0f968023c77e"
},
"execution_count": 95,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696827813.6782315\n",
"Mon Oct 9 05:03:33 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
}