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qsvr

eigenkit.qml.qsvr

eigenkit.qml.qsvr.QSVR

Bases: BaseQuantumRegressor

Quantum support vector regressor (QSVR).

It is the regression counterpart of QSVC. It uses SVR(kernel="precomputed") from scikit-learn on the quantum kernel matrix.

Parameters:

Name	Type	Description	Default
feature_map	str	The feature map. It is "Angle" (angle encoding into a product state) or "AngleCNOT" (angle encoding with ladders of controlled-NOT (CNOT) gates). Default: "Angle". It is ignored when kernel is given.	'Angle'
reps	int	The number of repetitions of the feature map. Default: 1. It is ignored when kernel is given.	1
C	float	The regularisation parameter of the support vector regressor. Default: 1.0.	1.0
epsilon	float	The width of the epsilon-insensitive tube. Default: 0.1.	0.1
provider	ProviderSpec	The backend. It takes the same options as Circuit(provider=...). Default: "numpy". It is ignored when kernel is given.	'numpy'
random_state	int | None	The seed.	42
kernel	QuantumKernel | None	A QuantumKernel to use instead of building one from feature_map, reps and provider. Default: None.	None
scale	float	The bandwidth. The inputs are multiplied by it before they reach the kernel. Default: 1.0.	1.0

Example

import numpy as np
from eigenkit import QSVR

rng = np.random.default_rng(0)
X_train, X_test = rng.uniform(0, np.pi, (8, 2)), rng.uniform(0, np.pi, (3, 2))
y_train = np.sin(X_train).sum(axis=1)

model = QSVR(feature_map="Angle", reps=1)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

fit(X, y)

Fit the regressor on training data.

Parameters:

Name	Type	Description	Default
X	ndarray	The samples, of shape (n_samples, n_features).	required
y	ndarray	The float targets, of shape (n_samples,).	required

Returns:

Type	Description
QSVR	This estimator, so that calls can be chained.

load(path, *, provider=None) classmethod

Load a fitted model from a .eigenkit bundle.

Load only bundles that you trust. A bundle can contain joblib pickles, and unpickling runs code, as with torch.load or joblib.load.

Parameters:

Name	Type	Description	Default
path	str | Path	The path of the bundle.	required
provider	ProviderSpec | None	A provider that replaces the saved one.	None

Returns:

Type	Description
'BaseQuantumEstimator'	The loaded estimator.

Raises:

Type	Description
TypeError	If the method is called on a subclass and the bundle holds a different class. BaseQuantumEstimator.load() accepts any class.

predict(X)

Predict continuous targets for X.

Parameters:

Name	Type	Description	Default
X	ndarray	The samples, of shape (n_samples, n_features).	required

Returns:

Type	Description
ndarray	One prediction for each sample.

save(path)

Save a fitted model to a .eigenkit bundle.

The bundles of models that hold scikit-learn objects (QSVC, QSVR, the reservoir estimators and the time-series estimators) contain joblib pickles, so loading one can run code. Share them only with people who trust you.

Parameters:

Name	Type	Description	Default
path	str | Path	The path of the bundle. The suffix .eigenkit is added when it is missing.	required