Quantum self-learning Monte Carlo with quantum Fourier transform sampler
Quantum Physics
2021-01-04 v1
Abstract
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this "quantum inspired" algorithm is demonstrated by some numerical simulations.
Cite
@article{arxiv.2005.14075,
title = {Quantum self-learning Monte Carlo with quantum Fourier transform sampler},
author = {Katsuhiro Endo and Taichi Nakamura and Keisuke Fujii and Naoki Yamamoto},
journal= {arXiv preprint arXiv:2005.14075},
year = {2021}
}
Comments
11 pages, 7 figures