基于动态量子算法的概率模型优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic model optimization framework based on dynamic quantum algorithms. Traditional probabilistic model optimization often relies on static parameters, limiting the potential for efficiency gains. Our proposed framework dynamically adjusts quantum state fields, optimizing quantum computation's performance through a continuous, adaptive process. We detail the core mechanism of this framework, outlining the utilization of a quantum algorithm for real-time field manipulation. The resulting optimizations demonstrate a significant improvement in quantum state fidelity and overall computational throughput compared to conventional approaches. This work offers a promising pathway for enhancing the performance of quantum algorithms, particularly those involving complex quantum circuits. The framework's adaptability and the potential for substantial efficiency gains make it a significant advancement in the field of quantum computing.

DOI: 10.5281/zenodo.22185756

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