{ "cells": [ { "cell_type": "markdown", "id": "intense-ecology", "metadata": {}, "source": [ "# Neural Network Classifier & Regressor\n", "\n", "In this tutorial we show how the `NeuralNetworkClassifier` and `NeuralNetworkRegressor` are used.\n", "Both take as an input a (Quantum) `NeuralNetwork` and leverage it in a specific context.\n", "In both cases we also provide a pre-configured variant for convenience, the Variational Quantum Classifier (`VQC`) and Variational Quantum Regressor (`VQR`). The tutorial is structured as follows:\n", "\n", "\n", "1. [Classification](#Classification) \n", " * Classification with an `EstimatorQNN`\n", " * Classification with a `SamplerQNN`\n", " * Variational Quantum Classifier (`VQC`)\n", " \n", " \n", "2. [Regression](#Regression)\n", " * Regression with an `EstimatorQNN`\n", " * Variational Quantum Regressor (`VQR`)" ] }, { "cell_type": "code", "execution_count": 1, "id": "functioning-sword", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from IPython.display import clear_output\n", "from qiskit import QuantumCircuit\n", "from qiskit.circuit import Parameter\n", "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap\n", "from qiskit_algorithms.optimizers import COBYLA, L_BFGS_B\n", "from qiskit_algorithms.utils import algorithm_globals\n", "\n", "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC\n", "from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR\n", "from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n", "from qiskit_machine_learning.circuit.library import QNNCircuit\n", "\n", "algorithm_globals.random_seed = 42" ] }, { "cell_type": "markdown", "id": "compact-divide", "metadata": {}, "source": [ "## Classification\n", "\n", "We prepare a simple classification dataset to illustrate the following algorithms." ] }, { "cell_type": "code", "execution_count": 2, "id": "short-pierre", "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_inputs = 2\n", "num_samples = 20\n", "X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1\n", "y01 = 1 * (np.sum(X, axis=1) >= 0) # in { 0, 1}\n", "y = 2 * y01 - 1 # in {-1, +1}\n", "y_one_hot = np.zeros((num_samples, 2))\n", "for i in range(num_samples):\n", " y_one_hot[i, y01[i]] = 1\n", "\n", "for x, y_target in zip(X, y):\n", " if y_target == 1:\n", " plt.plot(x[0], x[1], \"bo\")\n", " else:\n", " plt.plot(x[0], x[1], \"go\")\n", "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "religious-history", "metadata": {}, "source": [ "### Classification with an `EstimatorQNN`\n", "\n", "First we show how an `EstimatorQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `EstimatorQNN` is expected to return one-dimensional output in $[-1, +1]$. This only works for binary classification and we assign the two classes to $\\{-1, +1\\}$. To simplify the composition of parameterized quantum circuit from a feature map and an ansatz we can use the `QNNCircuit` class. " ] }, { "cell_type": "code", "execution_count": 3, "id": "ceed90df", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# construct QNN with the QNNCircuit's default ZZFeatureMap feature map and RealAmplitudes ansatz.\n", "qc = QNNCircuit(num_qubits=2)\n", "qc.draw(\"mpl\", style=\"clifford\")" ] }, { "cell_type": "markdown", "id": "formed-animal", "metadata": {}, "source": [ "Create a quantum neural network" ] }, { "cell_type": "code", "execution_count": 4, "id": "determined-hands", "metadata": {}, "outputs": [], "source": [ "estimator_qnn = EstimatorQNN(circuit=qc)" ] }, { "cell_type": "code", "execution_count": 5, "id": "acute-casting", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.23521988]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# QNN maps inputs to [-1, +1]\n", "estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))" ] }, { "cell_type": "markdown", "id": "stone-holiday", "metadata": {}, "source": [ "We will add a callback function called `callback_graph`. This will be called for each iteration of the optimizer and will be passed two parameters: the current weights and the value of the objective function at those weights. For our function, we append the value of the objective function to an array so we can plot iteration versus objective function value and update the graph with each iteration. However, you can do whatever you want with a callback function as long as it gets the two parameters mentioned passed. " ] }, { "cell_type": "code", "execution_count": 6, "id": "similar-controversy", "metadata": {}, "outputs": [], "source": [ "# callback function that draws a live plot when the .fit() method is called\n", "def callback_graph(weights, obj_func_eval):\n", " clear_output(wait=True)\n", " objective_func_vals.append(obj_func_eval)\n", " plt.title(\"Objective function value against iteration\")\n", " plt.xlabel(\"Iteration\")\n", " plt.ylabel(\"Objective function value\")\n", " plt.plot(range(len(objective_func_vals)), objective_func_vals)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "id": "lesser-receiver", "metadata": {}, "outputs": [], "source": [ "# construct neural network classifier\n", "estimator_classifier = NeuralNetworkClassifier(\n", " estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "adopted-editor", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.8" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit classifier to data\n", "estimator_classifier.fit(X, y)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score classifier\n", "estimator_classifier.score(X, y)" ] }, { "cell_type": "code", "execution_count": 9, "id": "civilian-analysis", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# evaluate data points\n", "y_predict = estimator_classifier.predict(X)\n", "\n", "# plot results\n", "# red == wrongly classified\n", "for x, y_target, y_p in zip(X, y, y_predict):\n", " if y_target == 1:\n", " plt.plot(x[0], x[1], \"bo\")\n", " else:\n", " plt.plot(x[0], x[1], \"go\")\n", " if y_target != y_p:\n", " plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "japanese-seattle", "metadata": {}, "source": [ "Now, when the model is trained, we can explore the weights of the neural network. Please note, the number of weights is defined by ansatz." ] }, { "cell_type": "code", "execution_count": 10, "id": "offshore-basket", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.86209107, -1.06526254, -0.10663602, -0.39086371, 1.0894299 ,\n", " 0.59368219, 2.22731471, -1.04769663])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimator_classifier.weights" ] }, { "cell_type": "markdown", "id": "determined-standing", "metadata": {}, "source": [ "### Classification with a `SamplerQNN`\n", "\n", "Next we show how a `SamplerQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `SamplerQNN` is expected to return $d$-dimensional probability vector as output, where $d$ denotes the number of classes. \n", "The underlying `Sampler` primitive returns quasi-distributions of bit strings and we just need to define a mapping from the measured bitstrings to the different classes. For binary classification we use the parity mapping. Again we can use the `QNNCircuit` class to set up a parameterized quantum circuit from a feature map and ansatz of our choice." ] }, { "cell_type": "code", "execution_count": 11, "id": "d1ff56f4", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# construct a quantum circuit from the default ZZFeatureMap feature map and a customized RealAmplitudes ansatz\n", "qc = QNNCircuit(ansatz=RealAmplitudes(num_inputs, reps=1))\n", "qc.draw(\"mpl\", style=\"clifford\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "young-sensitivity", "metadata": {}, "outputs": [], "source": [ "# parity maps bitstrings to 0 or 1\n", "def parity(x):\n", " return \"{:b}\".format(x).count(\"1\") % 2\n", "\n", "\n", "output_shape = 2 # corresponds to the number of classes, possible outcomes of the (parity) mapping." ] }, { "cell_type": "code", "execution_count": 13, "id": "statutory-mercury", "metadata": {}, "outputs": [], "source": [ "# construct QNN\n", "sampler_qnn = SamplerQNN(\n", " circuit=qc,\n", " interpret=parity,\n", " output_shape=output_shape,\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "hybrid-orlando", "metadata": {}, "outputs": [], "source": [ "# construct classifier\n", "sampler_classifier = NeuralNetworkClassifier(\n", " neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "id": "adult-newman", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.7" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit classifier to data\n", "sampler_classifier.fit(X, y01)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score classifier\n", "sampler_classifier.score(X, y01)" ] }, { "cell_type": "code", "execution_count": 16, "id": "angry-bulgarian", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# evaluate data points\n", "y_predict = sampler_classifier.predict(X)\n", "\n", "# plot results\n", "# red == wrongly classified\n", "for x, y_target, y_p in zip(X, y01, y_predict):\n", " if y_target == 1:\n", " plt.plot(x[0], x[1], \"bo\")\n", " else:\n", " plt.plot(x[0], x[1], \"go\")\n", " if y_target != y_p:\n", " plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "assisted-individual", "metadata": {}, "source": [ "Again, once the model is trained we can take a look at the weights. As we set `reps=1` explicitly in our ansatz, we can see less parameters than in the previous model." ] }, { "cell_type": "code", "execution_count": 17, "id": "indonesian-bulletin", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.67198565, 0.46045402, -0.93462862, -0.95266092])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampler_classifier.weights" ] }, { "cell_type": "markdown", "id": "champion-approval", "metadata": {}, "source": [ "### Variational Quantum Classifier (`VQC`)\n", "\n", "The `VQC` is a special variant of the `NeuralNetworkClassifier` with a `SamplerQNN`. It applies a parity mapping (or extensions to multiple classes) to map from the bitstring to the classification, which results in a probability vector, which is interpreted as a one-hot encoded result. By default, it applies this the `CrossEntropyLoss` function that expects labels given in one-hot encoded format and will return predictions in that format too." ] }, { "cell_type": "code", "execution_count": 18, "id": "legislative-dublin", "metadata": {}, "outputs": [], "source": [ "# construct feature map, ansatz, and optimizer\n", "feature_map = ZZFeatureMap(num_inputs)\n", "ansatz = RealAmplitudes(num_inputs, reps=1)\n", "\n", "# construct variational quantum classifier\n", "vqc = VQC(\n", " feature_map=feature_map,\n", " ansatz=ansatz,\n", " loss=\"cross_entropy\",\n", " optimizer=COBYLA(maxiter=30),\n", " callback=callback_graph,\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "id": "geographic-adjustment", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.8" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit classifier to data\n", "vqc.fit(X, y_one_hot)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score classifier\n", "vqc.score(X, y_one_hot)" ] }, { "cell_type": "code", "execution_count": 20, "id": "stopped-heavy", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# evaluate data points\n", "y_predict = vqc.predict(X)\n", "\n", "# plot results\n", "# red == wrongly classified\n", "for x, y_target, y_p in zip(X, y_one_hot, y_predict):\n", " if y_target[0] == 1:\n", " plt.plot(x[0], x[1], \"bo\")\n", " else:\n", " plt.plot(x[0], x[1], \"go\")\n", " if not np.all(y_target == y_p):\n", " plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "grave-testament", "metadata": {}, "source": [ "### Multiple classes with VQC\n", "In this section we generate an artificial dataset that contains samples of three classes and show how to train a model to classify this dataset. This example shows how to tackle more interesting problems in machine learning. Of course, for a sake of short training time we prepare a tiny dataset. We employ `make_classification` from SciKit-Learn to generate a dataset. There 10 samples in the dataset, 2 features, that means we can still have a nice plot of the dataset, as well as no redundant features, these are features are generated as a combinations of the other features. Also, we have 3 different classes in the dataset, each classes one kind of centroid and we set class separation to `2.0`, a slight increase from the default value of `1.0` to ease the classification problem.\n", "\n", "Once the dataset is generated we scale the features into the range `[0, 1]`." ] }, { "cell_type": "code", "execution_count": 21, "id": "plastic-dividend", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import make_classification\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "X, y = make_classification(\n", " n_samples=10,\n", " n_features=2,\n", " n_classes=3,\n", " n_redundant=0,\n", " n_clusters_per_class=1,\n", " class_sep=2.0,\n", " random_state=algorithm_globals.random_seed,\n", ")\n", "X = MinMaxScaler().fit_transform(X)" ] }, { "cell_type": "markdown", "id": "forced-disclosure", "metadata": {}, "source": [ "Let's see how our dataset looks like." ] }, { "cell_type": "code", "execution_count": 22, "id": "premier-drill", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X[:, 0], X[:, 1], c=y)" ] }, { "cell_type": "markdown", "id": "deadly-response", "metadata": {}, "source": [ "We also transform labels and make them categorical." ] }, { "cell_type": "code", "execution_count": 23, "id": "exposed-bailey", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n" ] } ], "source": [ "y_cat = np.empty(y.shape, dtype=str)\n", "y_cat[y == 0] = \"A\"\n", "y_cat[y == 1] = \"B\"\n", "y_cat[y == 2] = \"C\"\n", "print(y_cat)" ] }, { "cell_type": "markdown", "id": "instructional-headquarters", "metadata": {}, "source": [ "We create an instance of `VQC` similar to the previous example, but in this case we pass a minimal set of parameters. Instead of feature map and ansatz we pass just the number of qubits that is equal to the number of features in the dataset, an optimizer with a low number of iteration to reduce training time, a quantum instance, and a callback to observe progress." ] }, { "cell_type": "code", "execution_count": 24, "id": "latin-result", "metadata": {}, "outputs": [], "source": [ "vqc = VQC(\n", " num_qubits=2,\n", " optimizer=COBYLA(maxiter=30),\n", " callback=callback_graph,\n", ")" ] }, { "cell_type": "markdown", "id": "proper-bookmark", "metadata": {}, "source": [ "Start the training process in the same way as in previous examples." ] }, { "cell_type": "code", "execution_count": 25, "id": "reported-pioneer", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.9" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit classifier to data\n", "vqc.fit(X, y_cat)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score classifier\n", "vqc.score(X, y_cat)" ] }, { "cell_type": "markdown", "id": "weighted-renaissance", "metadata": {}, "source": [ "Despite we had the low number of iterations, we achieved quite a good score. Let see the output of the `predict` method and compare the output with the ground truth." ] }, { "cell_type": "code", "execution_count": 26, "id": "employed-patient", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted labels: ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'B']\n", "Ground truth: ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n" ] } ], "source": [ "predict = vqc.predict(X)\n", "print(f\"Predicted labels: {predict}\")\n", "print(f\"Ground truth: {y_cat}\")" ] }, { "cell_type": "markdown", "id": "guided-secret", "metadata": {}, "source": [ "## Regression\n", "\n", "We prepare a simple regression dataset to illustrate the following algorithms." ] }, { "cell_type": "code", "execution_count": 27, "id": "iraqi-flavor", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_samples = 20\n", "eps = 0.2\n", "lb, ub = -np.pi, np.pi\n", "X_ = np.linspace(lb, ub, num=50).reshape(50, 1)\n", "f = lambda x: np.sin(x)\n", "\n", "X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb\n", "y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)\n", "\n", "plt.plot(X_, f(X_), \"r--\")\n", "plt.plot(X, y, \"bo\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "talented-capitol", "metadata": {}, "source": [ "### Regression with an `EstimatorQNN`\n", "\n", "Here we restrict to regression with an `EstimatorQNN` that returns values in $[-1, +1]$. More complex and also multi-dimensional models could be constructed, also based on `SamplerQNN` but that exceeds the scope of this tutorial." ] }, { "cell_type": "code", "execution_count": 28, "id": "perfect-kelly", "metadata": {}, "outputs": [], "source": [ "# construct simple feature map\n", "param_x = Parameter(\"x\")\n", "feature_map = QuantumCircuit(1, name=\"fm\")\n", "feature_map.ry(param_x, 0)\n", "\n", "# construct simple ansatz\n", "param_y = Parameter(\"y\")\n", "ansatz = QuantumCircuit(1, name=\"vf\")\n", "ansatz.ry(param_y, 0)\n", "\n", "# construct a circuit\n", "qc = QNNCircuit(feature_map=feature_map, ansatz=ansatz)\n", "\n", "# construct QNN\n", "regression_estimator_qnn = EstimatorQNN(circuit=qc)" ] }, { "cell_type": "code", "execution_count": 29, "id": "velvet-marks", "metadata": {}, "outputs": [], "source": [ "# construct the regressor from the neural network\n", "regressor = NeuralNetworkRegressor(\n", " neural_network=regression_estimator_qnn,\n", " loss=\"squared_error\",\n", " optimizer=L_BFGS_B(maxiter=5),\n", " callback=callback_graph,\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "id": "working-mongolia", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.9769994291935522" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit to data\n", "regressor.fit(X, y)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score the result\n", "regressor.score(X, y)" ] }, { "cell_type": "code", "execution_count": 31, "id": "diverse-conservative", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot target function\n", "plt.plot(X_, f(X_), \"r--\")\n", "\n", "# plot data\n", "plt.plot(X, y, \"bo\")\n", "\n", "# plot fitted line\n", "y_ = regressor.predict(X_)\n", "plt.plot(X_, y_, \"g-\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "false-india", "metadata": {}, "source": [ "Similarly to the classification models, we can obtain an array of trained weights by querying a corresponding property of the model. In this model we have only one parameter defined as `param_y` above." ] }, { "cell_type": "code", "execution_count": 32, "id": "terminal-turner", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-1.58870599])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regressor.weights" ] }, { "cell_type": "markdown", "id": "offensive-legislation", "metadata": {}, "source": [ "### Regression with the Variational Quantum Regressor (`VQR`)\n", "\n", "Similar to the `VQC` for classification, the `VQR` is a special variant of the `NeuralNetworkRegressor` with a `EstimatorQNN`. By default it considers the `L2Loss` function to minimize the mean squared error between predictions and targets." ] }, { "cell_type": "code", "execution_count": 33, "id": "offensive-entry", "metadata": {}, "outputs": [], "source": [ "vqr = VQR(\n", " feature_map=feature_map,\n", " ansatz=ansatz,\n", " optimizer=L_BFGS_B(maxiter=5),\n", " callback=callback_graph,\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "id": "cooperative-helmet", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.9769955693935384" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create empty array for callback to store evaluations of the objective function\n", "objective_func_vals = []\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", "\n", "# fit regressor\n", "vqr.fit(X, y)\n", "\n", "# return to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "# score result\n", "vqr.score(X, y)" ] }, { "cell_type": "code", "execution_count": 35, "id": "genetic-cambridge", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot target function\n", "plt.plot(X_, f(X_), \"r--\")\n", "\n", "# plot data\n", "plt.plot(X, y, \"bo\")\n", "\n", "# plot fitted line\n", "y_ = vqr.predict(X_)\n", "plt.plot(X_, y_, \"g-\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "id": "backed-visit", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Version Information

SoftwareVersion
qiskit0.45.2
qiskit_machine_learning0.7.1
qiskit_algorithms0.2.2
System information
Python version3.10.13
Python compilerGCC 9.4.0
Python buildmain, Jan 10 2024 19:45:45
OSLinux
CPUs1
Memory (Gb)7.744113922119141
Wed Jan 24 06:42:51 2024 UTC
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

This code is a part of Qiskit

© Copyright IBM 2017, 2024.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tutorial_magics\n", "\n", "%qiskit_version_table\n", "%qiskit_copyright" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "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": "3.10.13" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }