`squlearn.kernel`.QKODE

class squlearn.kernel.QKODE(quantum_kernel: KernelMatrixBase | str, loss: KernelLossBase, optimizer: OptimizerBase, alpha_seed: int = 0, k_train: ndarray = None, dkdx_train: ndarray = None, dkdxdx_train: ndarray = None, **kwargs)

Quantum Kernel Ordinary Differential Equation (QKODE) solver.

This class implements a quantum kernel-based solver for ordinary differential equations (ODEs) using the mixed model regression method as described in Ref. [1].

Parameters:

quantum_kernel (Union[KernelMatrixBase, str]) – Quantum kernel to be used in the model. If set to “precomputed”, the derivatives of the kernel matrix have to be provided.
loss (KernelLossBase) – Loss function to be used for training the model.
optimizer (OptimizerBase) – Optimizer to be used for minimizing the loss function.
alpha_seed (int, default=0) – Seed for random initialization of dual coefficients.
k_train (np.ndarray) – Precomputed training kernel matrix of shape (n_train, n_train). Required if quantum_kernel is “precomputed”.
dkdx_train (np.ndarray) – Precomputed first derivatives of the training kernel matrix. Required if quantum_kernel is “precomputed”.
dkdxdx_train (np.ndarray) – Precomputed second derivatives of the training kernel matrix. Required if quantum_kernel is “precomputed” and the ODE is of order 2.
**kwargs – Additional keyword arguments to be passed to the base class.

Attributes:

dual_coeff (np.ndarray) :
Array containing the weight vector in kernel space.

k_train (np.ndarray) :
Training kernel matrix of shape (n_train, n_train) which is available after calling the fit procedure.

k_testtrain (np.ndarray) :
Kernel matrix of shape (n_test, n_train) which is evaluated at the predict step.

Methods:

dump(target: str | IO[bytes]) → None: Serializes the model object to a file or file-like object. :param target: The target file path or file-like object where the model will be serialized. :type target: Union[str, IO[bytes]]

fit(X, y)

Fit the Quantum Kernel ODE model.

Parameters:

X (np.ndarray) – Samples of data of shape (n_samples, n_features) used for fitting the QKODE model.
y (np.ndarray) – Labels of shape (n_samples,) used for fitting the QKODE model.

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 hyperparameters and their values of the QKRR method.

Parameters:: deep (bool) – If True, also the parameters for contained objects are returned (default=True).
Returns:: Dictionary with hyperparameters and values.

classmethod load(source: str | IO[bytes], executor: Executor) → T

Deserializes the model object from a file or file-like object, injecting the provided Executor. :param source: The source file path or file-like object from which the model will be deserialized. :type source: Union[str, IO[bytes]] :param executor: The Executor instance to be injected into the deserialized model. :type executor: Executor

Returns:: The deserialized model object with the injected Executor.

predict(X: ndarray) → ndarray

Predict using the Quantum Kernel Ridge model.

Parameters:: X (np.ndarray) – Samples of data of shape (n_samples, n_features) on which QKRR model makes predictions. If quantum_kernel == “precomputed” this is instead a precomputed (test-train) kernel matrix of shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting.
Returns:: Returns predicted labels (at X) of shape (n_samples,)
Return type:: np.ndarray

score(X, y, sample_weight=None)

Return coefficient of determination on test data.

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_params(**params) → None

Sets value of the encoding circuit hyperparameters.

Parameters:: params – Hyperparameters and their values, e.g. num_qubits=2.

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

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

squlearn.kernel.QKODE

Attributes:

Methods:

`squlearn.kernel`.QKODE