Bayesian Constraint Optimization for Quantum State Collapse

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum state collapse is a fundamental process in quantum mechanics, and its optimization represents a significant challenge for practical quantum computing. This paper explores the application of Bayesian constraint optimization to this problem, proposing a novel framework that dynamically adjusts collapse probabilities based on observation and internal feedback loops. We move beyond traditional probability density calculations by constructing a Bayesian network to continuously refine the collapse strategy. This approach offers a path towards more efficient and robust quantum state control, addressing limitations of conventional methods and paving the way for enhanced quantum algorithm performance. The paper details the theoretical foundation, implementation of the Bayesian network, and preliminary results demonstrating the framework's effectiveness in a simplified quantum system.

DOI: 10.5281/zenodo.22279139

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