| Autor: |
Junyu Liu, Changchun Zhong, Matthew Otten, Anirban Chandra, Cristian L Cortes, Chaoyang Ti, Stephen K Gray, Xu Han |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Machine Learning: Science and Technology, Vol 4, Iss 2, p 025003 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2632-2153 |
| DOI: |
10.1088/2632-2153/acc726 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
