Title: Bayesian Adaptive Constraint Optimization with Quantum Embedding

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel Bayesian Adaptive Constraint Optimization (BACO) algorithm, leveraging quantum embedding to enhance exploration and exploitation simultaneously. The core concept is to embed quantum states within the optimization process, enabling a quantum-informed exploration that dynamically adjusts constraint sets based on the *internal* quantum state of a system. This approach addresses limitations of traditional optimization techniques by simultaneously reducing computational cost and improving solution quality. The algorithm utilizes a Bayesian framework to refine constraint sets iteratively, guiding the search towards promising regions of the solution space. We demonstrate the effectiveness of this quantum embedding method through extensive simulations and analysis, showcasing a significant improvement in both solution quality and computational efficiency compared to existing state-of-the-art techniques. The resulting BACO algorithm offers a promising pathway towards more robust and efficient optimization in complex, high-dimensional problems.

DOI: 10.5281/zenodo.22271638

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