基于自适应的分布式量子计算算法优化框架

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Distributed quantum computing (DQC) offers immense potential for tackling complex computational problems, but its realization faces significant challenges related to algorithm design, parameter optimization, and scalability. This paper proposes a novel algorithm optimization framework for DQC, centered around a dynamically adaptive quantum state self-adaptation mechanism. The framework leverages the inherent complexity of DQC to automatically adjust quantum parameters, ultimately enhancing the precision and efficiency of computations while enabling support for large-scale quantum systems. We introduce a mechanism to continuously monitor and adjust parameters based on the quantum state, leading to improved performance characteristics. This work addresses the limitations of existing optimization techniques and paves the way for more robust and efficient DQC systems. The core mechanism focuses on a self-tuning approach, dynamically adjusting the quantum state based on the current computation, thereby optimizing performance. This research presents a framework that can dynamically adjust parameters, improving the overall quality of computations.

DOI: 10.5281/zenodo.22277009

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