3 papers
quant-ph2025
Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
Yuichiro Nakano, Ken N. Okada, Keisuke Fujii
Sampling problems are widely regarded as the task for which quantum computers can most readily provide a quantum advantage. Leveraging this feature, the quantum-enhanced Markov cha…
quant-ph2025
Optimal elemental configuration search in crystal using quantum approximate optimization algorithm
Kazuhide Ichikawa, Genta Hayashi, Satoru Ohuchi +3
Optimal elemental configuration search in crystal is a crucial task to discovering industrially important materials such as lithium-ion battery cathodes. In this paper we present a…
quant-ph2024
Scalable circuit depth reduction in feedback-based quantum optimization with a quadratic approximation
Don Arai, Ken N. Okada, Yuichiro Nakano +2
Combinatorial optimization problems are one of the areas where near-term noisy quantum computers may have practical advantage against classical computers. Recently a novel feedback…