Bayesian Constraint Optimization for Quantum State Design

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel Bayesian constraint optimization framework for quantum state design, aiming to accelerate the discovery of optimal quantum states through iterative refinement of parameters. The framework leverages a probabilistic model that weighs potential states based on observed entanglement and decoherence, utilizing a dynamically updated prior derived from repeated simulations. This approach addresses the limitations of traditional heuristics by incorporating a robust, probabilistic analysis, promising significant improvements in the efficiency of quantum state design. The core mechanism involves a Bayesian model that continuously updates the prior based on simulation results, guiding the search towards more promising states. The paper details the model architecture, the rationale behind the probabilistic weighting, and the simulation methodology used to demonstrate the framework's effectiveness. The results showcase a substantial reduction in the search space compared to conventional methods, highlighting the potential for significantly faster and more accurate quantum state design.

DOI: 10.5281/zenodo.22265426

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