Bayesian Constraint Optimization via Dynamic Quantum Entanglement

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel Bayesian framework for constraint optimization that utilizes dynamically entangled quantum systems to enhance exploration of solution space. Classical optimization techniques often struggle with high-dimensional problems due to their reliance on deterministic search and limited representation of complex solutions. We propose a quantum-inspired Bayesian approach, employing entangled qubits to iteratively refine a constraint set, enabling parallel exploration and capturing subtle correlations within the search space. This approach surpasses the limitations of classical optimization by offering a fundamentally new method for tackling complex, high-dimensional problems. The paper details the framework's architecture, explores its potential benefits, and presents preliminary results demonstrating its superior performance compared to existing methods.

DOI: 10.5281/zenodo.22265785

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