Quantum self-learning Monte Carlo with quantum Fourier transform sampler

• URL: http://arxiv.org/abs/2005.14075v1
• Date: Thu, 28 May 2020 15:16:00 GMT
• Title: Quantum self-learning Monte Carlo with quantum Fourier transform sampler
• Authors: Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii, and Naoki Yamamoto
• Abstract summary: This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. The performance of this "quantum inspired" algorithm is demonstrated by some numerical simulations.
• Score: 1.961783412203541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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