2020/05/28 by Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii +1
Computer Science · Physics and Astronomy · #Fourier transform #Hybrid Monte Carlo #Monte Carlo integration #Monte Carlo method #Quantum Computing Algorithms and Architecture #Quantum Fourier transform #Quantum Information and Cryptography #Quantum Monte Carlo #Quantum computer #Quantum many-body systems #Quantum phase estimation algorithm #Quasi-Monte Carlo method #quant-ph
paper · pdf · doi:10.1103/physrevresearch.2.043442
published as Phys. Rev. Research 2, 043442 (2020) · 11 pages, 7 figures
arxiv created 2020/05/28 · openalex created_date 2020/06/05 · openalex publication_date 2020/12/31 · arxiv updated 2021/01/04 · openalex updated_date 2026/08/05
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.