3 papers
quant-ph2026
Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
Ryo Suzuki, Shohei Watabe
Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method of…
quant-ph2024
Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches
Santaro Kikuura, Ryoya Igata, Yuta Shingu +1
This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems: max-cut, number partitioning, knapsack, and…
quant-ph2024
Can Constrained Quantum Annealing Be Effective in Noisy Quantum Annealers?
Ryoya Igata, Myonsok I, Yuya Seki +2
We investigate the performance of penalty-based quantum annealing (PQA) and constrained quantum annealing (CQA) in solving the graph partitioning problem under various noise models…