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qsvc

eigenkit.qml.qsvc

eigenkit.qml.qsvc.QSVC

Bases: BaseQuantumClassifier

Quantum support vector classifier (QSVC).

It computes a quantum kernel matrix from a fidelity circuit, and then uses SVC(kernel="precomputed") from scikit-learn.

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 machine. Default: 1.0.

 1.0
probability bool

If True, the model gives Platt-scaled probabilities. If False (the default), predict_proba is unavailable. The calibration is a sigmoid fitted by cross-validation on the training kernel, as in CalibratedClassifierCV(SVC(), ensemble=False). The predictions still come from the plain SVC.

 False
provider ProviderSpec

The backend. It takes the same options as Circuit(provider=...). Any backend with a statevector or a zero_state_probability method works, including Qiskit Runtime and IQM hardware. 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. It can be a custom kernel, a trained kernel or a hardware kernel. Default: None.

 None
scale float

The bandwidth. The inputs are multiplied by it before they reach the kernel, so it can be tuned with GridSearchCV. Default: 1.0.

 1.0
Example 
import numpy as np
from eigenkit import QSVC, Encoding, QuantumKernel, Variational

rng = np.random.default_rng(0)
X_train, X_test = rng.uniform(0, np.pi, (8, 4)), rng.uniform(0, np.pi, (3, 4))
y_train = np.array([0, 1] * 4)

model = QSVC(feature_map="AngleCNOT", reps=2)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

# Any kernel works, including custom and trained ones.
my_map = [Encoding(in_features=4), Variational(depth=1)]
model = QSVC(kernel=QuantumKernel(n_features=4, feature_map=my_map))

decision_function(X)

Return the signed distance of each sample to the separating hyperplane.

Parameters:

Name Type Description Default
X ndarray

The samples, of shape (n_samples, n_features).

 required

Returns:

Type Description
ndarray

An array of shape (n_samples,) for two classes, and (n_samples, n_classes) otherwise.

fit(X, y)

Fit the classifier on training data.

Parameters:

Name Type Description Default
X ndarray

The samples, of shape (n_samples, n_features).

 required
y ndarray

The class labels, of shape (n_samples,).

 required

Returns:

Type Description
QSVC

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 the class labels of 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.

predict_proba(X)

Predict the probability of each class. It requires probability=True.

Parameters:

Name Type Description Default
X ndarray

The samples, of shape (n_samples, n_features).

 required

Returns:

Type Description
ndarray

The probabilities, of shape (n_samples, n_classes), with the columns in the order of classes_.

Raises:

Type Description
ValueError

If the model was created with probability=False.

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