Bayesian Constraint Optimization for Quantum Complexity

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum complexity is a critical challenge in the development of quantum algorithms, demanding precise control over quantum states and minimizing the inherent uncertainty. Traditional optimization approaches often fail to effectively address this challenge, requiring extensive trial and error. This paper proposes a novel Bayesian Constraint Optimization (BCO) framework to address this limitation by incorporating a dynamic Bayesian network (DBN) that continuously evolves the optimization model based on observed error signals. The BDM allows for a more adaptive and robust algorithm design, potentially leading to significant improvements in quantum algorithm performance. We demonstrate the efficacy of this approach through a series of simulations exploring the design of various quantum circuits and demonstrating its ability to outperform existing static optimization techniques. This work represents a significant advancement in quantum algorithm design, moving beyond traditional methods and embracing a data-driven, Bayesian perspective.

DOI: 10.5281/zenodo.22270885

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