arXiv subjects

Takuma Yoshihara

Publications and source records attributed to Takuma Yoshihara.

3 recordsLinked to original sources

arXiv2026

Accelerating Extended Benders Decomposition with Quantum-Classical Hybrid Solver

We propose a quantum-classical hybrid method for solving large-scale mixed-integer quadratic problems (MIQP). Although extended Benders decomposition is effective for MIQP, its master problem which handles the integer and quadratic variables often becomes a computational bottleneck. To address this challenge, we integrate the D-Wave CQM solver into the decomposition framework to solve the master problem directly. Our results show that this hybrid approach efficiently yields near-optimal solutions and, for certain problem instances, achieves exponential speedups over the leading commercial classical solver. These findings highlight a promising computational strategy for tackling complex mixed-integer optimization problems.

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arXiv2025

Efficient Construction of Feasible Solutions in Column Generation using Quantum Annealing

Column generation (CG) has been used to solve constrained 0-1 quadratic programming problems. The pricing problem, which is iteratively solved in CG, can be reduced to an unconstrained 0-1 quadratic programming problem, allowing for the efficient application of quantum annealing (QA). The solutions obtained by CG are continuous relaxations, which cannot be practically used as feasible 0-1 solutions. In this paper, we propose a postprocessing method for constructing feasible 0-1 solutions from the continuous relaxations obtained through CG. The proposed technique consists of two phases: (i) mapping the continuous CG solution to a feasible 0-1 solution and (ii) applying a constraint-aware local search to improve that solution's quality. Numerical experiments on randomly generated problems demonstrate that CG with the proposed postprocessing yields solutions comparable to commercial solvers with significantly reduced computation time. Consequently, the postprocessing enables CG with QA to obtain high-quality approximate solutions faster.

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arXiv2023

Individual subject evaluated difficulty of adjustable mazes generated using quantum annealing

In this paper, the maze generation using quantum annealing is proposed. We reformulate a standard algorithm to generate a maze into a specific form of a quadratic unconstrained binary optimization problem suitable for the input of the quantum annealer. To generate more difficult mazes, we introduce an additional cost function $Q_{update}$ to increase the difficulty. The difficulty of the mazes was evaluated by the time to solve the maze of 12 human subjects. To check the efficiency of our scheme to create the maze, we investigated the time-to-solution of a quantum processing unit, classical computer, and hybrid solver.

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