Quantum-Based Probabilistic Optimization Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic optimization algorithm leveraging the unique capabilities of quantum computing. Traditional optimization techniques often struggle with large-scale problems, limiting their applicability. We propose a quantum-enhanced algorithm designed to accelerate and improve the solution of complex optimization tasks. This algorithm utilizes quantum mechanics principles to enhance the search process, leading to more efficient and accurate results. The core mechanism centers around quantum superposition and entanglement to explore solution spaces in a fundamentally different manner. We detail the algorithm's design, the quantum circuit implementation, and the resulting performance improvements compared to classical approaches. The paper concludes with a discussion of the potential implications of this approach for various real-world optimization challenges.

DOI: 10.5281/zenodo.22445999

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