Quantum Decoherence-Driven Adaptive Hardware Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum hardware optimization leveraging the inherent characteristics of quantum decoherence. The core idea is to construct an adaptive system that continuously monitors and corrects quantum errors arising from decoherence, ultimately enhancing the performance and stability of quantum computations. The proposed method utilizes a feedback loop where the degree of decoherence experienced by quantum bits is measured and quantified. This measurement serves as an optimization signal, which is then processed by a reinforcement learning algorithm. The algorithm dynamically adjusts hardware parameters – such as pulse shapes, durations, and frequencies – to minimize the detrimental effects of decoherence and maximize the preservation of quantum superposition and coherence. Unlike conventional hardware optimization techniques that rely on offline calibration or fixed algorithms, this approach offers a real-time, adaptive response to decoherence, representing a significant advancement in quantum computing hardware development. The effectiveness of this strategy is predicated on the ability to accurately characterize and respond to decoherence in a manner that directly mitigates its impact on quantum computations. The research delves into the mathematical framework underpinning this adaptive system, detailing the relationships between measurement data, reinforcement learning parameters, and hardware adjustments.

DOI: 10.5281/zenodo.22200069

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