论文标题
量子Kerr学习
Quantum Kerr Learning
论文作者
论文摘要
量子机学习是一个快速发展的研究领域，可以促进量子计算的重要应用，并显着影响数据驱动的科学。在我们的工作中，基于复杂性理论和物理学的各种论点，我们证明，在处理基于内核的方法时，单个Kerr模式可以提供一些“量子增强”。使用内核特性，神经切线核理论，Kerr非线性的一阶扰动理论以及非扰动数值模拟，我们表明，量子增强可能会在收敛时间和概括性误差方面发生。此外，我们对如何考虑如何考虑更高维度的输入数据有明确的指示。最后，我们提出了一个实验协议，我们根据电路QED称为\ emph {量子kerr学习}。
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.
