Quantum Kerr learning
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.
- Publication:
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Machine Learning: Science and Technology
- Pub Date:
- June 2023
- DOI:
- 10.1088/2632-2153/acc726
- arXiv:
- arXiv:2205.12004
- Bibcode:
- 2023MLS&T...4b5003L
- Keywords:
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- quantum machine learning;
- machine learning theory;
- quantum physics;
- Quantum Physics;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 20 pages, many figures. v2: significant updates, author added