Liu, Junyu and Zhong, Changchun and Otten, Matthew and Chandra, Anirban and Cortes, Cristian L and Ti, Chaoyang and K Gray, Stephen and Han, Xu (2023) Quantum Kerr learning. Machine Learning: Science and Technology, 4 (2). 025003. ISSN 2632-2153
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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 quantum Kerr learning, based on circuit QED.
Item Type: | Article |
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Subjects: | East Asian Archive > Multidisciplinary |
Depositing User: | Unnamed user with email support@eastasianarchive.com |
Date Deposited: | 14 Jul 2023 11:47 |
Last Modified: | 18 May 2024 08:55 |
URI: | http://library.eprintdigipress.com/id/eprint/1253 |