QuASK – Quantum Advantage Seeker with Kernels
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QuASK is a quantum machine learning software written in Python that supports researchers in designing, experimenting, and assessing different quantum and classical kernels performance. This software is package agnostic and can be integrated with all major quantum software packages (e.g. IBM Qiskit, Xanadu's Pennylane, Amazon Braket). QuASK guides the user through a simple preprocessing of input data, definition and calculation of quantum and classical kernels, either custom or pre-defined ones. From this evaluation the package provides an assessment about potential quantum advantage and prediction bounds on generalization error. Moreover, it allows for the generation of parametric quantum kernels that can be trained using gradient-descent-based optimization, grid search, or genetic algorithms. Projected quantum kernels, an effective solution to mitigate the curse of dimensionality induced by the exponential scaling dimension of large Hilbert spaces, are also calculated. QuASK can furthermore generate the observable values of a quantum model and use them to study the prediction capabilities of the quantum and classical kernels.
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