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Quantum-Inspired Constraint Optimization (QICO)

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum-Inspired Constraint Optimization (QICO) presents a novel approach to optimization that draws inspiration from quantum mechanics, specifically superposition and entanglement. This paper explores the potential of utilizing these principles to accelerate the search for optimal solutions by representing and manipulating solution space in a fundamentally different manner than conventional algorithms. We detail the core mechanism of QICO, outlining its theoretical underpinnings and preliminary experimental results demonstrating significant performance improvements in a range of optimization problems. The paper concludes with a discussion of future research directions and the potential impact of this approach on diverse fields.

DOI
10.5281/zenodo.22194244

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Quantum-Inspired Constraint Programming (QICP) represents a novel approach to constraint programming, leveraging principles of quantum mechanics to enhance solution exploration and computational efficiency. This paper proposes a framework inspired by quantum phenomena, specifically superposition and entanglement, to represent and manipulate constraints. We demonstrate the effectiveness of this method through a series of optimized solutions for a challenging, multi-constrained optimization problem, highlighting its potential to significantly improve performance compared to conventional techniques. The core mechanism focuses on utilizing superposition to represent multiple constraint combinations simultaneously, facilitating efficient search and reducing the number of potential solutions. This paper provides a comprehensive analysis of the algorithm's design and implementation, along with a detailed evaluation of its performance.
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