Quantum-Accelerated Optimization for Constraint Satisfaction in High-Dimensional Spaces

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-accelerated optimization is a rapidly developing field with the potential to revolutionize complex optimization problems, particularly those encountered in areas like constraint satisfaction and resource allocation. This paper investigates the application of quantum algorithms to constraint satisfaction problems (CSPs) in high-dimensional spaces, focusing on the design of a quantum approach that promises exponential speedups over classical methods. We propose a novel quantum annealing approach, incorporating specific parameters and optimization strategies, to address the inherent challenges of large-scale CSPs. This work presents a theoretical framework, outlines the key steps of the algorithm, and discusses the potential for practical implementation. The goal is to demonstrate the feasibility of quantum acceleration for this challenging optimization task, contributing to advancements in the field of quantum computation and optimization.

DOI: 10.5281/zenodo.22274997

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