squlearn.kernel.ml.QSVR

class squlearn.kernel.ml.QSVR(quantum_kernel: KernelMatrixBase | str | None = None, **kwargs)

Quantum Support Vector Regression

This class is a wrapper of sklearn.svm.SVR. It uses a quantum kernel matrix to replace the kernel matrix in the sklearn.svm.SVR class. The parameters of the parent class can be adjusted via **kwargs. See the documentation there for additional information about the standard SVR parameters. The scikit-learn SVR has kernel specific arguments that are omitted here because they do not apply to the quantum kernels. These are

  • kernel

  • gamma

  • degree

  • coef0

Parameters:

  • quantum_kernel (Union[KernelMatrixBase, str]) – The quantum kernel matrix to be used in the SVC. Either a fidelity quantum kernel (FQK) or projected quantum kernel (PQK) must be provided. By setting quantum_kernel=”precomputed”, X is assumed to be a kernel matrix (train and test-train). This is particularly useful when storing quantum kernel matrices from real backends to numpy arrays.

  • **kwargs – Possible arguments can be obtained by calling get_params(). Notable examples are parameters of the sklearn.svm.SVR class such as the regularization parameters C (float, default=1.0) or epsilon (float, default=0.1). Additionally, properties of the underlying encoding circuit can be adjusted via kwargs such as the number of qubits (num_qubits), or (if supported) the number of layers (num_layers).

See also

squlearn.kernel.ml.QSVC

Quantum Support Vector Classification

Example

import numpy as np

from sklearn.model_selection import train_test_split

from squlearn import Executor
from squlearn.encoding_circuit import HubregtsenEncodingCircuit
from squlearn.kernel.ml.qsvr import QSVR
from squlearn.kernel.matrix import ProjectedQuantumKernel

encoding_circuit = HubregtsenEncodingCircuit(num_qubits=2, num_features=1, num_layers=2)
kernel = ProjectedQuantumKernel(
    encoding_circuit,
    executor=Executor(),
    initial_parameters=np.random.rand(encoding_circuit.num_parameters))

X = np.linspace(0, np.pi, 100)
y = np.sin(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

qsvc = QSVR(quantum_kernel=kernel)
qsvc.fit(X_train, y_train)
print(f"The score on the test set is {qsvc.score(X_test, y_test)}")

Methods:

fit(X, y, sample_weight=None)

Fit the QSVR model according to the given training data.

Parameters:

  • X (array-like, sparse matrix) – Training data of shape (n_samples, n_features) or (n_samples, n_samples) Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).

  • y (array-like) – Lables with shape (n_samples,)

  • sample_weight (array-like) – Weights of shape (n_samples,), default=None Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

Returns an instance of self.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep: bool = True) → dict

Returns hyper-parameters and their values of the QSVR class.

Parameters:

deep (bool) – If True, also the parameters for contained objects are returned (default=True).

Returns:

Dictionary with hyper-parameters and values.

predict(X)

Perform regression on samples in X.

For an one-class model, +1 (inlier) or -1 (outlier) is returned.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns:

y_pred – The predicted values.

Return type:

ndarray of shape (n_samples,)

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → QSVR

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params) → None

Sets value of the QSVR hyper-parameters.

Parameters:

params – Hyper-parameters and their values, e.g. num_qubits=2.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → QSVR

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object