6 papers
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…
Physics-Aware Linearized ADMM and Its Unrolling
Satoshi Takabe, Shunta Arai, Tadashi Wadayama
Recently, partial differential equations (PDEs) have been used to directly model the measurement process in signal processing, although their evaluation is costly. In this paper, w…
Refined Gradient-Based Temperature Optimization for the Replica-Exchange Monte-Carlo Method
Tatsuya Miyata, Shunta Arai, Satoshi Takabe
The replica-exchange Monte-Carlo (RXMC) method is a powerful Markov-chain Monte-Carlo algorithm for sampling from multi-modal distributions, which are challenging for conventional…
Quantum Annealing Enhanced Markov-Chain Monte Carlo
Shunta Arai, Tadashi Kadowaki
In this study, we propose quantum annealing-enhanced Markov Chain Monte Carlo (QAEMCMC), where QA is integrated into the MCMC subroutine. QA efficiently explores low-energy configu…
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…
Deep Unfolded Local Quantum Annealing
Shunta Arai, Satoshi Takabe
Local quantum annealing (LQA), an iterative algorithm, is designed to solve combinatorial optimization problems. It draws inspiration from QA, which utilizes adiabatic time evoluti…