Qsvm

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QSVM

Quantum support vector machine (QSVM) model. Classifies classical data into two categories. The model is first trained, and fitted. After pre-training, the model predicts the labels of new data points. Methods:

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feature_map

feature_map = feature_map

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num_qubits

num_qubits = num_qubits

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execution_preferences

execution_preferences = execution_preferences

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kernel_eval

kernel_eval = QuantumKernelEvaluator(feature_map=(self.feature_map), num_qubits=(self.num_qubits), execution_preferences=(self.execution_preferences))

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model

model = SVC(kernel='precomputed')

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train_data

train_data: np.ndarray | None = None

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train

train(
self: ,
train_data: np.ndarray,
train_labels: np.ndarray
) -> None

Trains an SVM model using a custom precomputed kernel from the training data. Parameters:

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predict

predict(
self: ,
data: np.ndarray
) -> np.ndarray

Predicts labels for new data using a precomputed kernel with a trained SVM model. Evaluates kernel matrix elements which are associated with the support vectors (those associated with non-vanishing coefficients in the prediction equation). Parameters: Returns:

• Type: np.ndarray
• np.ndarray: Predicted labels (0,1).

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test

test(
self: ,
data: np.ndarray,
data_labels: np.ndarray
) -> tuple[float, np.ndarray]

Predicts the labels of the test dataset and evaluates the resulting test score using the ground-truth labels. Parameters: Returns:

• Type: tuple[float, np.ndarray]
• containing test score (float) and test labels (np.ndarray[int]).

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get_svm_model

get_svm_model(
self:
) -> SVC

Returns the classical SVM model. Parameters:

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get_qprog

get_qprog(
self: ,
data_dim: int = 0
) -> QuantumProgram

Returns the quantum program for the kernel at data_dim feature width. Parameters: