Predicting open quantum dynamics with data-informed quantum-classical dynamics

Oral-In-person

Abstract

We introduce a data-informed quantum-classical dynamics  (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. 

We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (Calcium Fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (Rubrene) with accuracy comparable to nearly exact numerical methods.

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Publication: Xie, P., Wang, K., Mitra, A., Zhu, Y., Li, X., de Jong, W. A., & Yang, C. (2025). Predict open quantum dynamics with data-informed quantum-classical dynamics. arXiv preprint arXiv:2508.17170.

Presenters

  • Pinchen Xie

    • Lawrence Berkeley National Lab

Authors

  • Pinchen Xie

    • Lawrence Berkeley National Lab

  • Ke Wang

  • Anupam Mitra

    • Lawrence Berkeley National Laboratory

  • Yuanran Zhu

    • Lawrence Berkeley National Laboratory

  • Xiantao Li

    • Pennsylvania State University

  • Wibe De Jong

    • Lawrence Berkeley National Laboratory

  • Chao Yang

    • Lawrence Berkeley Lab