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Quantum Kerr learning
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DOI：10.1088/2632-2153/acc726.png)
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Abstract
En 中文
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.
Keywords:
quantum machine learning
machine learning theory
quantum physics
Journal
M
IF:
4.6
Papers:
1.1K
Citations:
3.4K
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