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arXiv · 2205.12004

Quantum Kerr Learning

Junyu Liu·Changchun Zhong·Matthew Otten·Anirban Chandra·Cristian L. Cortes·Chaoyang Ti·Stephen K Gray·Xu Han
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Abstract

Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some "quantum enhancements" when dealing with kernel-based methods. Using kernel properties, neural tangent kernel theory, first-order perturbation theory of the Kerr non-linearity, and non-perturbative numerical simulations, we show that quantum enhancements could happen in terms of convergence time and generalization error. Furthermore, we make explicit indications on how higher-dimensional input data could be considered. Finally, we propose an experimental protocol, that we call \emph{quantum Kerr learning}, based on circuit QED.

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quant-phcs.AIcs.LGstat.ML

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BibTeXRIS

Junyu Liu, Changchun Zhong, Matthew Otten, Anirban Chandra, Cristian L. Cortes, Chaoyang Ti, Stephen K Gray, Xu Han. 2022-05-20. Quantum Kerr Learning. https://doi.org/10.1088/2632-2153%2Facc726

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