Title: Adaptive Quantum Computing Probability Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the application of adaptive quantum computing probability optimization to enhance the reliability of quantum computations. Quantum systems are inherently probabilistic, and maintaining stable and accurate quantum states is a significant challenge. Traditional approaches often rely on fixed probability distributions, which can lead to suboptimal performance and increased error rates. This research explores the use of dynamic algorithms to dynamically adjust quantum probability distributions, offering a novel approach to improve qubit coherence and reduce errors. The core mechanism focuses on iterative refinement of the probability distribution, guided by a feedback loop that adapts to the observed quantum state. The proposed method aims to achieve a significant improvement in reliability and ultimately, higher computational accuracy.

DOI: 10.5281/zenodo.22289449

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