Quantum Topologies: Probability Model Optimization via Quantum Computing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Topologies represent a fundamentally new class of topological quantum states exhibiting unique properties, offering a potential paradigm shift in computation and information processing. This paper explores the application of quantum computing to optimize probability models for quantum systems, aiming to enhance predictive accuracy. We propose a novel approach utilizing quantum algorithms to build and refine probability models, leveraging the inherent properties of quantum systems to achieve a higher degree of precision. The core mechanism involves simulating quantum topology systems with quantum computers, generating probabilistic models, and employing quantum optimization techniques to refine these models, ultimately improving the accuracy of predictions. The research presents a new perspective on probability model optimization, capitalizing on quantum computing's potential to address limitations in classical approaches.

DOI: 10.5281/zenodo.22204442

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