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Efficiency Metrics for Parallel QML Algorithms /
10Q7L 4RYe5RYt Epochs /
45 Epoch 100.0% 2511.44s kkawchak.ipynb
1009 lines (1008 with data), 86.7 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "gDf49Zs-YG8S",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "b18fb77c-a34b-4a8d-982b-2366e159d9e8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1704815788.6913338\n",
"Tue Jan 9 15:56:28 2024\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",
"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": "markdown",
"metadata": {
"id": "m4zD_EUsYG8Y"
},
"source": [
"Turning quantum nodes into Torch Layers\n",
"=======================================\n",
"\n",
"::: {.meta}\n",
":property=\\\"og:description\\\": Learn how to create hybrid ML models in\n",
"PennyLane using Torch :property=\\\"og:image\\\":\n",
"<https://pennylane.ai/qml/_images/PyTorch_icon.png>\n",
":::\n",
"\n",
"::: {.related}\n",
"tutorial\\_qnn\\_module\\_tf Turning quantum nodes into Keras Layers\n",
":::\n",
"\n",
"*Author: Tom Bromley --- Posted: 02 November 2020. Last updated: 28\n",
"January 2021.*\n",
"\n",
"Creating neural networks in [PyTorch](https://pytorch.org/) is easy\n",
"using the [nn module](https://pytorch.org/docs/stable/nn.html). Models\n",
"are constructed from elementary *layers* and can be trained using the\n",
"PyTorch API. For example, the following code defines a two-layer network\n",
"that could be used for binary classification:\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "ELkMIhhMYG8c"
},
"outputs": [],
"source": [
"from pennylane import broadcast\n",
"import torch\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"\n",
"layer_1 = torch.nn.Linear(2, 2)\n",
"layer_2 = torch.nn.Linear(2, 2)\n",
"softmax = torch.nn.Softmax(dim=1)\n",
"\n",
"layers = [layer_1, layer_2, softmax]\n",
"model = torch.nn.Sequential(*layers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ym9INdgjYG8d"
},
"source": [
"**What if we want to add a quantum layer to our model?** This is\n",
"possible in PennyLane:\n",
"`QNodes <../glossary/hybrid_computation>`{.interpreted-text role=\"doc\"}\n",
"can be converted into `torch.nn` layers and combined with the wide range\n",
"of built-in classical [layers](https://pytorch.org/docs/stable/nn.html)\n",
"to create truly hybrid models. This tutorial will guide you through a\n",
"simple example to show you how it\\'s done!\n",
"\n",
"::: {.note}\n",
"::: {.title}\n",
"Note\n",
":::\n",
"\n",
"A similar demo explaining how to\n",
"`turn quantum nodes into Keras layers <tutorial_qnn_module_tf>`{.interpreted-text\n",
"role=\"doc\"} is also available.\n",
":::\n",
"\n",
"Fixing the dataset and problem\n",
"==============================\n",
"\n",
"Let us begin by choosing a simple dataset and problem to allow us to\n",
"focus on how the hybrid model is constructed. Our objective is to\n",
"classify points generated from scikit-learn\\'s binary-class\n",
"[make\\_moons()](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)\n",
"dataset:\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
},
"id": "DJnE2JiyYG8d",
"outputId": "f1b845e8-cbc4-4f0c-fae1-de69e1cdb17c"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.datasets import make_moons\n",
"\n",
"# Set random seeds\n",
"torch.manual_seed(42)\n",
"np.random.seed(42)\n",
"\n",
"X, y = make_moons(n_samples=200, noise=0.1)\n",
"y_ = torch.unsqueeze(torch.tensor(y), 1) # used for one-hot encoded labels\n",
"y_hot = torch.scatter(torch.zeros((200, 2)), 1, y_, 1)\n",
"\n",
"c = [\"#1f77b4\" if y_ == 0 else \"#ff7f0e\" for y_ in y] # colours for each class\n",
"plt.axis(\"off\")\n",
"plt.scatter(X[:, 0], X[:, 1], c=c)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QwENIn2CYG8e"
},
"source": [
"Defining a QNode\n",
"================\n",
"\n",
"Our next step is to define the QNode that we want to interface with\n",
"`torch.nn`. Any combination of device, operations and measurements that\n",
"is valid in PennyLane can be used to compose the QNode. However, the\n",
"QNode arguments must satisfy additional `conditions\n",
"<code/api/pennylane.qnn.TorchLayer>`{.interpreted-text role=\"doc\"}\n",
"including having an argument called `inputs`. All other arguments must\n",
"be arrays or tensors and are treated as trainable weights in the model.\n",
"We fix a two-qubit QNode using the\n",
"`default.qubit <code/api/pennylane.devices.default_qubit.DefaultQubit>`{.interpreted-text\n",
"role=\"doc\"} simulator and operations from the\n",
"`templates <introduction/templates>`{.interpreted-text role=\"doc\"}\n",
"module.\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"id": "TO-sU02MYG8f"
},
"outputs": [],
"source": [
"import pennylane as qml\n",
"\n",
"n_qubits = 10\n",
"dev = qml.device(\"default.qubit\", wires=n_qubits)\n",
"\n",
"@qml.qnode(dev)\n",
"def qnode(inputs, weights):\n",
" qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation='Y')\n",
" qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation='Y')\n",
" qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation='Y')\n",
" qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation='Y')\n",
" qml.RandomLayers(weights, wires=range(n_qubits), ratio_imprim=0, rotations=[qml.RY], seed=42)\n",
" return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]\n",
"weights = np.array([1,2,3,4,5,6,7,8,9,10]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vwo-ucc4YG8g"
},
"source": [
"Interfacing with Torch\n",
"======================\n",
"\n",
"With the QNode defined, we are ready to interface with `torch.nn`. This\n",
"is achieved using the `~pennylane.qnn.TorchLayer`{.interpreted-text\n",
"role=\"class\"} class of the `~pennylane.qnn`{.interpreted-text\n",
"role=\"mod\"} module, which converts the QNode to the elementary building\n",
"block of `torch.nn`: a *layer*. We shall see in the following how the\n",
"resultant layer can be combined with other well-known neural network\n",
"layers to form a hybrid model.\n",
"\n",
"We must first define the `weight_shapes` dictionary. Recall that all of\n",
"the arguments of the QNode (except the one named `inputs`) are treated\n",
"as trainable weights. For the QNode to be successfully converted to a\n",
"layer in `torch.nn`, we need to provide the details of the shape of each\n",
"trainable weight for them to be initialized. The `weight_shapes`\n",
"dictionary maps from the argument names of the QNode to corresponding\n",
"shapes:\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "O-RFeXqmYG8h"
},
"outputs": [],
"source": [
"n_layers = 5\n",
"weight_shapes = {\"weights\": (n_layers, n_qubits)}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1uMLnLnGYG8h"
},
"source": [
"In our example, the `weights` argument of the QNode is trainable and has\n",
"shape given by `(n_layers, n_qubits)`, which is passed to\n",
"`~pennylane.templates.layers.BasicEntanglerLayers`{.interpreted-text\n",
"role=\"func\"}.\n",
"\n",
"Now that `weight_shapes` is defined, it is easy to then convert the\n",
"QNode:\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "m5fcIRAWYG8i"
},
"outputs": [],
"source": [
"qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p9-wiN-0YG8i"
},
"source": [
"With this done, the QNode can now be treated just like any other\n",
"`torch.nn` layer and we can proceed using the familiar Torch workflow.\n",
"\n",
"Creating a hybrid model\n",
"=======================\n",
"\n",
"Let\\'s create a basic three-layered hybrid model consisting of:\n",
"\n",
"1. a 2-neuron fully connected classical layer\n",
"2. our 2-qubit QNode converted into a layer\n",
"3. another 2-neuron fully connected classical layer\n",
"4. a softmax activation to convert to a probability vector\n",
"\n",
"A diagram of the model can be seen in the figure below.\n",
"\n",
"{.align-center\n",
"width=\"100.0%\"}\n",
"\n",
"We can construct the model using the\n",
"[Sequential](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html)\n",
"API:\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"id": "Pgc-TkPpYG8j"
},
"outputs": [],
"source": [
"clayer_1 = torch.nn.Linear(2, 10)\n",
"clayer_2 = torch.nn.Linear(10, 2)\n",
"softmax = torch.nn.Softmax(dim=1)\n",
"layers = [clayer_1, qlayer, clayer_2, softmax]\n",
"model = torch.nn.Sequential(*layers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "suZkU2tEYG8j"
},
"source": [
"Training the model\n",
"==================\n",
"\n",
"We can now train our hybrid model on the classification dataset using\n",
"the usual Torch approach. We\\'ll use the standard\n",
"[SGD](https://pytorch.org/docs/stable/optim.html#torch.optim.SGD)\n",
"optimizer and the mean absolute error loss function:\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "57MwgNnEYG8j"
},
"outputs": [],
"source": [
"opt = torch.optim.SGD(model.parameters(), lr=0.2)\n",
"loss_func = torch.nn.L1Loss()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Y_yohUlYG8j"
},
"source": [
"Note that there are more advanced combinations of optimizer and loss\n",
"function, but here we are focusing on the basics.\n",
"\n",
"The model is now ready to be trained!\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"id": "NtA9KgZ-YG8k",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "0f6bbcb0-9bb9-4026-a6f2-9b8f029e6510"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train - Epoch 1: Loss: 0.1927\n",
"Train Accuracy: 94.00%\n",
"Test - Epoch 1: Loss: 0.0990\n",
"Test Accuracy: 94.00%\n",
"Train - Epoch 2: Loss: 0.0650\n",
"Train Accuracy: 98.00%\n",
"Test - Epoch 2: Loss: 0.0470\n",
"Test Accuracy: 98.00%\n",
"Train - Epoch 3: Loss: 0.0380\n",
"Train Accuracy: 98.00%\n",
"Test - Epoch 3: Loss: 0.0369\n",
"Test Accuracy: 98.00%\n",
"Train - Epoch 4: Loss: 0.0313\n",
"Train Accuracy: 98.00%\n",
"Test - Epoch 4: Loss: 0.0289\n",
"Test Accuracy: 98.00%\n",
"Train - Epoch 5: Loss: 0.0273\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 5: Loss: 0.0226\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 6: Loss: 0.0249\n",
"Train Accuracy: 98.50%\n",
"Test - Epoch 6: Loss: 0.0213\n",
"Test Accuracy: 98.50%\n",
"Train - Epoch 7: Loss: 0.0182\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 7: Loss: 0.0226\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 8: Loss: 0.0185\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 8: Loss: 0.0224\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 9: Loss: 0.0203\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 9: Loss: 0.0144\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 10: Loss: 0.0169\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 10: Loss: 0.0157\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 11: Loss: 0.0153\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 11: Loss: 0.0155\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 12: Loss: 0.0193\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 12: Loss: 0.0121\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 13: Loss: 0.0145\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 13: Loss: 0.0177\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 14: Loss: 0.0154\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 14: Loss: 0.0125\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 15: Loss: 0.0169\n",
"Train Accuracy: 98.50%\n",
"Test - Epoch 15: Loss: 0.0284\n",
"Test Accuracy: 98.50%\n",
"Train - Epoch 16: Loss: 0.0131\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 16: Loss: 0.0104\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 17: Loss: 0.0133\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 17: Loss: 0.0104\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 18: Loss: 0.0110\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 18: Loss: 0.0156\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 19: Loss: 0.0107\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 19: Loss: 0.0109\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 20: Loss: 0.0150\n",
"Train Accuracy: 99.00%\n",
"Test - Epoch 20: Loss: 0.0117\n",
"Test Accuracy: 99.00%\n",
"Train - Epoch 21: Loss: 0.0170\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 21: Loss: 0.0097\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 22: Loss: 0.0124\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 22: Loss: 0.0089\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 23: Loss: 0.0155\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 23: Loss: 0.0083\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 24: Loss: 0.0097\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 24: Loss: 0.0102\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 25: Loss: 0.0078\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 25: Loss: 0.0083\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 26: Loss: 0.0090\n",
"Train Accuracy: 99.00%\n",
"Test - Epoch 26: Loss: 0.0180\n",
"Test Accuracy: 99.00%\n",
"Train - Epoch 27: Loss: 0.0117\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 27: Loss: 0.0077\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 28: Loss: 0.0093\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 28: Loss: 0.0077\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 29: Loss: 0.0079\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 29: Loss: 0.0075\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 30: Loss: 0.0100\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 30: Loss: 0.0087\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 31: Loss: 0.0079\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 31: Loss: 0.0103\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 32: Loss: 0.0082\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 32: Loss: 0.0070\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 33: Loss: 0.0089\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 33: Loss: 0.0085\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 34: Loss: 0.0082\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 34: Loss: 0.0081\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 35: Loss: 0.0064\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 35: Loss: 0.0101\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 36: Loss: 0.0097\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 36: Loss: 0.0081\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 37: Loss: 0.0090\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 37: Loss: 0.0109\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 38: Loss: 0.0153\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 38: Loss: 0.0056\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 39: Loss: 0.0067\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 39: Loss: 0.0062\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 40: Loss: 0.0086\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 40: Loss: 0.0086\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 41: Loss: 0.0075\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 41: Loss: 0.0055\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 42: Loss: 0.0060\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 42: Loss: 0.0083\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 43: Loss: 0.0090\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 43: Loss: 0.0053\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 44: Loss: 0.0070\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 44: Loss: 0.0052\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 45: Loss: 0.0091\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 45: Loss: 0.0115\n",
"Test Accuracy: 99.50%\n"
]
}
],
"source": [
"X = torch.tensor(X, requires_grad=True).float()\n",
"y_hot = y_hot.float()\n",
"\n",
"batch_size = 5\n",
"batches = 200 // batch_size\n",
"\n",
"train_dataset = TensorDataset(X, y_hot)\n",
"train_loader = DataLoader(train_dataset, batch_size=5, shuffle=True, drop_last=True)\n",
"\n",
"epochs = 45\n",
"\n",
"for epoch in range(epochs):\n",
" train_running_loss = 0\n",
"\n",
" for xs, ys in train_loader:\n",
" opt.zero_grad()\n",
"\n",
" loss_evaluated = loss_func(model(xs), ys)\n",
" loss_evaluated.backward()\n",
"\n",
" opt.step()\n",
"\n",
" train_running_loss += loss_evaluated.item()\n",
"\n",
" train_avg_loss = train_running_loss / batches\n",
" print(f\"Train - Epoch {epoch + 1}: Loss: {train_avg_loss:.4f}\")\n",
"\n",
" # Calculate train accuracy\n",
" y_pred_train = model(X)\n",
" predictions_train = torch.argmax(y_pred_train, axis=1).detach().numpy()\n",
"\n",
" correct_train = [1 if p == p_true else 0 for p, p_true in zip(predictions_train, y)]\n",
" train_accuracy = sum(correct_train) / len(correct_train)\n",
" print(f\"Train Accuracy: {train_accuracy * 100:.2f}%\")\n",
"\n",
" # Calculate test loss and accuracy\n",
" test_loss = loss_func(model(X), y_hot).item()\n",
" print(f\"Test - Epoch {epoch + 1}: Loss: {test_loss:.4f}\")\n",
"\n",
" y_pred_test = model(X)\n",
" predictions_test = torch.argmax(y_pred_test, axis=1).detach().numpy()\n",
"\n",
" correct_test = [1 if p == p_true else 0 for p, p_true in zip(predictions_test, y)]\n",
" test_accuracy = sum(correct_test) / len(correct_test)\n",
" print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "orKWhPb5YG8k"
},
"source": [
"How did we do? The model looks to have successfully trained and the\n",
"accuracy is reasonably high. In practice, we would aim to push the\n",
"accuracy higher by thinking carefully about the model design and the\n",
"choice of hyperparameters such as the learning rate.\n",
"\n",
"Creating non-sequential models\n",
"==============================\n",
"\n",
"The model we created above was composed of a sequence of classical and\n",
"quantum layers. This type of model is very common and is suitable in a\n",
"lot of situations. However, in some cases we may want a greater degree\n",
"of control over how the model is constructed, for example when we have\n",
"multiple inputs and outputs or when we want to distribute the output of\n",
"one layer into multiple subsequent layers.\n",
"\n",
"Suppose we want to make a hybrid model consisting of:\n",
"\n",
"1. a 4-neuron fully connected classical layer\n",
"2. a 2-qubit quantum layer connected to the first two neurons of the\n",
" previous classical layer\n",
"3. a 2-qubit quantum layer connected to the second two neurons of the\n",
" previous classical layer\n",
"4. a 2-neuron fully connected classical layer which takes a\n",
" 4-dimensional input from the combination of the previous quantum\n",
" layers\n",
"5. a softmax activation to convert to a probability vector\n",
"\n",
"A diagram of the model can be seen in the figure below.\n",
"\n",
"{.align-center\n",
"width=\"100.0%\"}\n",
"\n",
"This model can also be constructed by creating a new class that inherits\n",
"from the `torch.nn`\n",
"[Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) and\n",
"overriding the `forward()` method:\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"id": "3nRj296bYG8k"
},
"outputs": [],
"source": [
"class HybridModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.clayer_1 = torch.nn.Linear(2, 70)\n",
" self.qlayer_1 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_2 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_3 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_4 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_5 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_6 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.qlayer_7 = qml.qnn.TorchLayer(qnode, weight_shapes)\n",
" self.clayer_2 = torch.nn.Linear(70, 2)\n",
" self.softmax = torch.nn.Softmax(dim=1)\n",
"\n",
" def forward(self, x):\n",
" x = self.clayer_1(x)\n",
" x_1, x_2, x_3, x_4, x_5, x_6, x_7 = torch.split(x, 10, dim=1)\n",
" x_1 = self.qlayer_1(x_1)\n",
" x_2 = self.qlayer_2(x_2)\n",
" x_3 = self.qlayer_3(x_3)\n",
" x_4 = self.qlayer_4(x_4)\n",
" x_5 = self.qlayer_5(x_5)\n",
" x_6 = self.qlayer_6(x_6)\n",
" x_7 = self.qlayer_7(x_7)\n",
" x = torch.cat([x_1, x_2, x_3, x_4, x_5, x_6, x_7], axis=1)\n",
" x = self.clayer_2(x)\n",
" return self.softmax(x)\n",
"\n",
"model = HybridModel()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XU2-uOcYYG8l"
},
"source": [
"As a final step, let\\'s train the model to check if it\\'s working:\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "0TOzLpJBYG8l",
"outputId": "aae4490b-e018-49a7-87c3-2cdf7ccdcb9c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train - Epoch 1: Loss: 0.1296\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 1: Loss: 0.0292\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 2: Loss: 0.0239\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 2: Loss: 0.0149\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 3: Loss: 0.0141\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 3: Loss: 0.0112\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 4: Loss: 0.0106\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 4: Loss: 0.0095\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 5: Loss: 0.0099\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 5: Loss: 0.0081\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 6: Loss: 0.0086\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 6: Loss: 0.0071\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 7: Loss: 0.0077\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 7: Loss: 0.0065\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 8: Loss: 0.0071\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 8: Loss: 0.0059\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 9: Loss: 0.0064\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 9: Loss: 0.0054\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 10: Loss: 0.0061\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 10: Loss: 0.0050\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 11: Loss: 0.0055\n",
"Train Accuracy: 99.50%\n",
"Test - Epoch 11: Loss: 0.0047\n",
"Test Accuracy: 99.50%\n",
"Train - Epoch 12: Loss: 0.0056\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 12: Loss: 0.0044\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 13: Loss: 0.0054\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 13: Loss: 0.0038\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 14: Loss: 0.0050\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 14: Loss: 0.0037\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 15: Loss: 0.0047\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 15: Loss: 0.0034\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 16: Loss: 0.0045\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 16: Loss: 0.0033\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 17: Loss: 0.0043\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 17: Loss: 0.0029\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 18: Loss: 0.0032\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 18: Loss: 0.0029\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 19: Loss: 0.0038\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 19: Loss: 0.0027\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 20: Loss: 0.0037\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 20: Loss: 0.0024\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 21: Loss: 0.0032\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 21: Loss: 0.0028\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 22: Loss: 0.0033\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 22: Loss: 0.0022\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 23: Loss: 0.0025\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 23: Loss: 0.0021\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 24: Loss: 0.0029\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 24: Loss: 0.0020\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 25: Loss: 0.0026\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 25: Loss: 0.0023\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 26: Loss: 0.0020\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 26: Loss: 0.0018\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 27: Loss: 0.0023\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 27: Loss: 0.0017\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 28: Loss: 0.0023\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 28: Loss: 0.0019\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 29: Loss: 0.0020\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 29: Loss: 0.0019\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 30: Loss: 0.0020\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 30: Loss: 0.0019\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 31: Loss: 0.0018\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 31: Loss: 0.0014\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 32: Loss: 0.0018\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 32: Loss: 0.0019\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 33: Loss: 0.0019\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 33: Loss: 0.0015\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 34: Loss: 0.0014\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 34: Loss: 0.0014\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 35: Loss: 0.0017\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 35: Loss: 0.0013\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 36: Loss: 0.0016\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 36: Loss: 0.0012\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 37: Loss: 0.0015\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 37: Loss: 0.0012\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 38: Loss: 0.0016\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 38: Loss: 0.0012\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 39: Loss: 0.0014\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 39: Loss: 0.0011\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 40: Loss: 0.0013\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 40: Loss: 0.0011\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 41: Loss: 0.0014\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 41: Loss: 0.0011\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 42: Loss: 0.0013\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 42: Loss: 0.0010\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 43: Loss: 0.0013\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 43: Loss: 0.0010\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 44: Loss: 0.0012\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 44: Loss: 0.0010\n",
"Test Accuracy: 100.00%\n",
"Train - Epoch 45: Loss: 0.0012\n",
"Train Accuracy: 100.00%\n",
"Test - Epoch 45: Loss: 0.0010\n",
"Test Accuracy: 100.00%\n"
]
}
],
"source": [
"opt = torch.optim.SGD(model.parameters(), lr=0.2)\n",
"epochs = 45\n",
"\n",
"for epoch in range(epochs):\n",
" train_running_loss = 0\n",
"\n",
" for xs, ys in train_loader:\n",
" opt.zero_grad()\n",
"\n",
" loss_evaluated = loss_func(model(xs), ys)\n",
" loss_evaluated.backward()\n",
"\n",
" opt.step()\n",
"\n",
" train_running_loss += loss_evaluated.item()\n",
"\n",
" train_avg_loss = train_running_loss / batches\n",
" print(f\"Train - Epoch {epoch + 1}: Loss: {train_avg_loss:.4f}\")\n",
"\n",
" # Calculate train accuracy\n",
" y_pred_train = model(X)\n",
" predictions_train = torch.argmax(y_pred_train, axis=1).detach().numpy()\n",
"\n",
" correct_train = [1 if p == p_true else 0 for p, p_true in zip(predictions_train, y)]\n",
" train_accuracy = sum(correct_train) / len(correct_train)\n",
" print(f\"Train Accuracy: {train_accuracy * 100:.2f}%\")\n",
"\n",
" # Calculate test loss and accuracy\n",
" test_loss = loss_func(model(X), y_hot).item()\n",
" print(f\"Test - Epoch {epoch + 1}: Loss: {test_loss:.4f}\")\n",
"\n",
" y_pred_test = model(X)\n",
" predictions_test = torch.argmax(y_pred_test, axis=1).detach().numpy()\n",
"\n",
" correct_test = [1 if p == p_true else 0 for p, p_true in zip(predictions_test, y)]\n",
" test_accuracy = sum(correct_test) / len(correct_test)\n",
" print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5hYXv8CkYG8l"
},
"source": [
"Great! We\\'ve mastered the basics of constructing hybrid\n",
"classical-quantum models using PennyLane and Torch. Can you think of any\n",
"interesting hybrid models to construct? How do they perform on realistic\n",
"datasets?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9QsGJNbCYG8m"
},
"source": [
"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": "MNv-uf3nGrec",
"outputId": "3733083f-a950-4d8f-a51c-4bd738c397d3"
},
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1704818300.1346135\n",
"Tue Jan 9 16:38:20 2024\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": [],
"machine_shape": "hm"
}
},
"nbformat": 4,
"nbformat_minor": 0
}