基于自适应的概率场论的量子算法

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm based on an adaptive probability field theory (APFT) that automatically adjusts quantum particle parameters to optimize quantum computation performance and enhance the efficiency of quantum algorithms. Traditional quantum algorithms often require manual parameter tuning, which is a time-consuming and error-prone process. Our proposed algorithm leverages adaptive parameter adjustment driven by quantum algorithm optimization to achieve significant performance improvements and stability in quantum computations. The core mechanism involves a dynamic adjustment of the quantum state, guided by a feedback loop that continuously evaluates the algorithm's progress and adjusts parameters to maintain optimal performance. This approach offers a substantial improvement over existing methods, potentially unlocking new capabilities in quantum computation.

DOI: 10.5281/zenodo.22251125

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