Probability and Quantum Computing Hybrid Algorithm Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel hybrid algorithm framework combining the strengths of probability logic and quantum computing to address complex optimization problems. We propose a system architecture that leverages the inherent scalability and parallel processing capabilities of quantum computing to enhance the robustness and efficiency of probabilistic optimization algorithms. The core mechanism involves integrating probabilistic logic modules with quantum computation layers, allowing for the efficient handling of large and complex problem instances. This framework aims to achieve significantly improved accuracy and scalability compared to traditional optimization methods, particularly when tackling problems with inherent uncertainties and combinatorial nature. The study explores the potential benefits of this integration, highlighting the advantages of combining these two powerful computational paradigms. We present a preliminary implementation demonstrating the framework's efficacy in a specific optimization scenario.

DOI: 10.5281/zenodo.22200711

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