Title: Quantum-Driven Evolutionary Computation for Constraint Satisfaction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-driven evolutionary computation offers a promising approach to solving complex constraint satisfaction problems (CSPs) by leveraging the unique capabilities of quantum annealing. This paper explores the integration of quantum annealing with evolutionary computation to achieve significantly faster and more efficient search for optimal solutions. We detail the design of a hybrid algorithm that combines the strengths of both paradigms, focusing on the generation of candidate solutions, refinement through evolutionary operators, and the efficient exploration of the solution space. The core mechanism centers around quantum annealing's ability to efficiently address high-dimensional search spaces, coupled with the evolutionary algorithm's capacity to explore diverse solutions and adapt to changing problem landscapes. This research investigates the effectiveness of this hybrid approach in tackling challenging CSPs, aiming to surpass the limitations of traditional classical methods.

DOI: 10.5281/zenodo.22188567

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