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Quantum Machine Learning Quantum Optimization

Accelerating Extended Benders Decomposition with Quantum-Classical Hybrid Solver

arXiv
Authors: Takuma Yoshihara, Masayuki Ohzeki

Year

2025

Paper ID

51865

Status

Preprint

Abstract Read

~2 min

Abstract Words

94

Citations

N/A

Abstract

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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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • We propose a quantum-classical hybrid method for solving large-scale mixed-integer quadratic problems (MIQP).

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