4 citations · 4 across the 2 of their papers we have counts for
3 papers
quant-ph2026
Slack-Free Deep-Unfolded Combinatorial Optimization Solver for Inequality Constraints
Ryo Hagiwara, Shunta Arai, Satoshi Takabe
Quantum annealing (QA) is used to solve combinatorial optimization problems (COPs). When COPs are implemented on quantum annealers, they are typically encoded as quadratic unconstr…
quant-ph2025
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer
Ryo Hagiwara, Shunta Arai, Satoshi Takabe
Quantum annealing (QA) has attracted research interest as a sampler and combinatorial optimization problem (COP) solver. A recently proposed sampling-based solver for QA significan…
cond-mat.dis-nn2024★ 4 cited
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding
Ryo Hagiwara, Satoshi Takabe
This study proposes a trainable sampling-based solver for combinatorial optimization problems (COPs) using a deep-learning technique called deep unfolding. The proposed solver is b…