Machine-learning-inspired quantum control in many-body dynamics

Meng Yun Mao, Zheng Cheng, Liangsheng Li, Ning Wu, Wen Long You

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1 Citation (Scopus)

Abstract

Achieving precise preparation of quantum many-body states is crucial for the practical implementation of quantum computation and quantum simulation. However, the inherent challenges posed by unavoidable excitations at critical points during quench processes necessitate careful design of control fields. In this work, we introduce a promising and versatile dynamic control neural network tailored to optimize control fields. We address the problem of suppressing defect density and enhancing cat-state fidelity during the passage across the critical point in the quantum Ising model. Our method facilitates seamless transitions between different objective functions by adjusting the optimization strategy. In comparison to gradient-based power-law quench methods, our approach demonstrates significant advantages for both small system sizes and long-term evolutions. We provide a detailed analysis of the specific forms of control fields and summarize common features for experimental implementation. Furthermore, numerical simulations demonstrate the robustness of our proposal against random noise and spin number fluctuations. The optimized defect density and cat-state fidelity exhibit a transition at a critical ratio of the quench duration to the system size, coinciding with the quantum speed limit for quantum evolution.

Original languageEnglish
Article number042428
JournalPhysical Review A
Volume109
Issue number4
DOIs
Publication statusPublished - Apr 2024

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Mao, M. Y., Cheng, Z., Li, L., Wu, N., & You, W. L. (2024). Machine-learning-inspired quantum control in many-body dynamics. Physical Review A, 109(4), Article 042428. https://doi.org/10.1103/PhysRevA.109.042428