Adaptive Parallel Algorithm Scheduler Based on Quantum Degradation Analysis

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel adaptive parallel algorithm scheduler designed to optimize the execution of quantum algorithms on imperfect quantum hardware. The core idea leverages quantum degradation analysis to dynamically adjust the scheduling of tasks and resource allocation. Traditional parallel algorithm schedulers often operate under the assumption of ideal quantum hardware, failing to account for the inherent noise and errors present in real-world quantum devices. Our approach utilizes real-time monitoring of quantum hardware degradation, specifically focusing on metrics like single-qubit fidelity, to guide the scheduling process. The scheduler employs a feedback loop, continuously assessing hardware performance and adapting the algorithm execution order and resource assignment accordingly. This adaptive strategy aims to mitigate the impact of quantum noise and maximize the overall fidelity of the final computation. The theoretical framework is presented, and the underlying mathematical concepts are detailed. The effectiveness of the proposed scheduler is illustrated through a conceptual model, showcasing its ability to improve performance in the presence of varying levels of quantum degradation. The primary contribution lies in the integration of quantum degradation analysis directly into the scheduling process, leading to a more robust and efficient execution of parallel quantum algorithms.

DOI: 10.5281/zenodo.22595576

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