Title: Quantum Entanglement-Based Constraint Satisfaction with Adaptive Relaxation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement offers a potentially transformative approach to constraint satisfaction problems (CSPs), enabling exponentially faster exploration of solution spaces compared to classical methods. This paper investigates the application of entanglement-based constraint satisfaction algorithms, focusing on the design of a novel constraint satisfaction algorithm that leverages quantum error correction to facilitate efficient solution discovery. We propose a mechanism utilizing entangled state vectors to represent potential solutions, enabling accelerated exploration of the solution space. The algorithm dynamically adapts relaxation parameters to optimize solutions, achieving significant improvements in computational efficiency. This work explores the fundamental principles of entanglement and its potential to revolutionize CSPs, presenting a framework for harnessing quantum phenomena to solve complex optimization challenges.

DOI: 10.5281/zenodo.22300647

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