Title: Quantum-Inspired Constraint Propagation for Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired constraint propagation offers a fundamentally new approach to optimization, leveraging the principles of quantum superposition to dramatically accelerate the search for optimal solutions. This paper presents a novel algorithm that utilizes a quantum superposition mechanism to explore the solution space more effectively, potentially leading to significant speedups in complex optimization problems. We explore the theoretical foundations of this approach and demonstrate its efficacy through a series of simulations and analyses. The core mechanism focuses on strategically collapsing superposition states to guide the search towards promising regions of the solution space. The paper concludes with a detailed analysis of the algorithm's performance and its potential impact on various optimization challenges.

DOI: 10.5281/zenodo.22186292

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