Keisuke Fujii

Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler

2020 · Physical Review Research

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 …

Optimizing a quantum reservoir computer for time series prediction

2020 · Scientific Reports

Quantum computing and neural networks show great promise for the future of information processing. In this paper we study a quantum reservoir computer (QRC), a framework harnessing quantum dynamics and designed for fast and efficient …

Pricing multi-asset derivatives by variational quantum algorithms

2022 · arXiv (Cornell University)

Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differential equations to calculate the prices of certain types of derivatives, the …

Hierarchical Quantum Error Correction with Hypergraph Product Code and Rotated Surface Code

2025 · Progress of Theoretical and Experimental Physics

Abstract We propose and analyze a hierarchical quantum error correction (QEC) scheme that concatenates hypergraph product (HGP) codes with rotated surface codes and that is compatible with quantum computers with only nearest-neighbor interactions. The outer …

Large-scale quantum device benchmarking via LXEB with particle-number-conserving random quantum circuits

2025 · Physical Review Research

Linear cross-entropy benchmarking (LXEB) with random quantum circuits is a standard method for evaluating quantum computers. However, LXEB requires classically simulating the ideal output distribution of a given quantum circuit with high numerical precision, which …