Naoki Yamamoto

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 …

Quantum-enhanced neural networks in the neural tangent kernel framework

2021 · arXiv (Cornell University)

Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cNN), but their practical and theoretically-guaranteed performance is still to be investigated. …

Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies With Higher Order Formulations

2024 · IEEE Transactions on Quantum Engineering

Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates …