Learning and forecasting open quantum dynamics with correlated noise

Learning and forecasting open quantum dynamics with correlated noise

Abstract The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficie...

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Main Authors: Xinfang Zhang, Zhihao Wu, Gregory A. L. White, Zhongcheng Xiang, Shun Hu, Zhihui Peng, Yong Liu, Dongning Zheng, Xiang Fu, Anqi Huang, Dario Poletti, Kavan Modi, Junjie Wu, Mingtang Deng, Chu Guo
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Communications Physics
Online Access:https://doi.org/10.1038/s42005-025-01944-2
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https://doi.org/10.1038/s42005-025-01944-2

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