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This study provides a rigorous analytical assessment of how specific decoherence channels—amplitude and phase damping—affect quantum reinforcement learning algorithms. Rather than viewing noise solely as an obstacle, the research identifies conditions where these environmental factors can inadvertently enhance learning dynamics within QML frameworks. These findings are critical for advancing fault-tolerant quantum machine learning by redefining strategies to leverage rather than just mitigate unavoidable physical imperfections.
Impact of Amplitude and Phase Damping Noise on Quantum Reinforcement Learning: Challenges and Opportunities
arXiv:2503.24069v2 Announce Type: replace Abstract: Quantum machine learning (QML) is an emerging field with significant potential, yet it remains highly susceptible to noise, which poses a major challenge to its practical implementation. While various noise mitigation strategies have been proposed to enhance algorithmic performance, the impact of noise is not fully understood. In this work, we investigate the effects of amplitude and phase damping noise on a quantum reinforcement learning algorithm. Through analytical and numerical analysis, we assess how these noise sources influence the learning process and overall performance. Our findings contribute to a deeper understanding of the role of noise in quantum learning algorithms and suggest that, rather than being purely detrimental, unavoidable noise may present opportunities to enhance QML processes.
Abstract:Quantum machine learning (QML) is an emerging field with significant potential, yet it remains highly susceptible to noise, which poses a major challenge to its practical implementation. While various noise mitigation strategies have been proposed to enhance algorithmic performance, the impact of noise is not fully understood. In this work, we investigate the effects of amplitude and phase damping noise on a quantum reinforcement learning algorithm. Through analytical and numerical analysis, we assess how these noise sources influence the learning process and overall performance. Our findings contribute to a deeper understanding of the role of noise in quantum learning algorithms and suggest that, rather than being purely detrimental, unavoidable noise may present opportunities to enhance QML processes.
arXiv:2503.24069 [quant-ph]
(or arXiv:2503.24069v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2503.24069 Focus to learn more arXiv-issued DOI via DataCite
Eur. Phys. J. Spec. Top. 235, 2991 (2026)
https://doi.org/10.1140/epjs/s11734-025-01760-3 Focus to learn more DOI(s) linking to related resources
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