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qek.kernel

source package qek.kernel

The Quantum Evolution Kernel itself, for use in a machine-learning pipeline.

Classes

  • FastQEK — FastQEK class.

  • IntegratedQEK — A variant of the Quantum Evolution Kernel that supports fit/transform/fit_transform from raw data (graphs).

source class FastQEK(mu: float, size_max: int | None = None, similarity: Callable[[NDArray[np.floating], NDArray[np.floating]], np.floating] | None = None)

Bases : BaseKernel[ProcessedData]

FastQEK class.

Initialize the kernel.

Attributes

  • - X (Sequence[ProcessedData]) — Training data used for fitting the kernel.

  • - kernel_matrix (np.ndarray) — Kernel matrix. This is assigned in the fit() method

Training parameters

mu (float): Scaling factor for the Jensen-Shannon divergence size_max (int, optional): If specified, only consider the first size_max qubits of bitstrings. Otherwise, consider all qubits. You may use this to trade precision in favor of speed.

Parameters

  • mu : float — Scaling factor for the Jensen-Shannon divergence

  • size_max : int, optional — If specified, only consider the first size_max qubits of bitstrings. Otherwise, consider all qubits. You may use this to trade precision in favor of speed.

  • similarity : optional — If specified, a custom similarity metric to use. Otherwise, use the Jensen-Shannon divergence.

Note: This class does not accept raw data, but rather ProcessedData. See class IntegratedQEK for a subclass that provides a more powerful API, at the expense of performance.

Methods

source method FastQEK.to_processed_data(X: Sequence[ProcessedData]) → Sequence[ProcessedData]

Convert the raw data into features.

source class IntegratedQEK(mu: float, extractor: BaseExtractor[GraphType], size_max: int | None = None, similarity: Callable[[NDArray[np.floating], NDArray[np.floating]], np.floating] | None = None)

Bases : BaseKernel[GraphType]

A variant of the Quantum Evolution Kernel that supports fit/transform/fit_transform from raw data (graphs).

Initialize an IntegratedQEK

Performance note

This class uses an extractor to convert the raw data into features. This can be very slow if you use, for instance, a remote QPU, as the waitlines to access a QPU can be very long. If you are using this in an interactive application or a server, this will block the entire thread during the wait.

We recommend using this class only with local emulators.

Training parameters

mu (float): Scaling factor for the Jensen-Shannon divergence extractor: An extractor (e.g. a QPU or a Quantum emulator) used to conver the raw data (graphs) into features. size_max (int, optional): If specified, only consider the first size_max qubits of bitstrings. Otherwise, consider all qubits. You may use this to trade precision in favor of speed. similarity (optional): If specified, a custom similarity metric to use. Otherwise, use the Jensen-Shannon divergence.

Parameters

  • mu : float — Scaling factor for the Jensen-Shannon divergence

  • extractor : BaseExtractor[GraphType] — An extractor (e.g. a QPU or a Quantum emulator) used to conver the raw data (graphs) into features.

  • size_max : int, optional — If specified, only consider the first size_max qubits of bitstrings. Otherwise, consider all qubits. You may use this to trade precision in favor of speed.

  • similarity : optional — If specified, a custom similarity metric to use. Otherwise, use the Jensen-Shannon divergence.

Methods

source method IntegratedQEK.to_processed_data(X: Sequence[GraphType]) → Sequence[ProcessedData]

Convert the raw data into features.

Performance note

This method can can be very slow if you use, for instance, a remote QPU, as the waitlines to access a QPU can be very long. If you are using this in an interactive application or a server, this will block the entire thread during the wait.