Bayesian Constraint Propagation with Quantum Entanglement

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Bayesian Constraint Propagation (BCP) offers a powerful approach to optimization by iteratively refining a set of constraints. This work proposes a novel framework utilizing quantum entanglement to enhance the process of constraint propagation, aiming for significantly reduced computational cost in complex optimization problems. We detail a quantum-enhanced measurement and entanglement scheme designed to explore the solution space more efficiently, effectively leveraging quantum properties to accelerate convergence and improve solution accuracy. The core mechanism involves representing constraints as entangled states, enabling a more nuanced and efficient exploration of the solution space. This research explores the potential of quantum entanglement to address limitations in traditional BCP, potentially unlocking new capabilities for tackling challenging optimization scenarios.

DOI: 10.5281/zenodo.22270165

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