## `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. | | `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. | | `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 ```python 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)` 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. | | `provider` | \`ProviderSpec | None\` | A provider that replaces the saved one. | 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. |