Quantum System Identification

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Transformer-Based Time-Series Inference of Lindblad Dynamics in Open Quantum Systems
Oct 5, 2026Julian Guam, Jianqing LiuQuantum System Identification System Identification
Department of Computer Science, North Carolina State University, Raleigh, NC
Simulation-Based Quantum System Inference with Neural Posterior Estimation
Sep 28, 2026Hang Zou, Anton Frisk Kockum, Martin Rahm +1Simulation-Based Inference Neural Posterior Estimation
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, 41296 Gothenburg, Sweden · Department of Microtechnology and Nanoscience, Chalmers University of Technology, 41296 Gothenburg, Sweden · Department of Chemistry and Chemical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden
Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements
Jul 25, 2026Taiqi Zhou, Weiyuan GongQuantum System Identification System Identification
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China · School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA
Learning the structure of open quantum systems
Jun 29, 2026Laura Lewis, Ewin Tang, John WrightQuantum System Identification Quantum Machine Learning
UC Berkeley
Optimal Ansatz-free Hamiltonian Learning In Situ
Jun 17, 2026Taiqi Zhou, Weiyuan GongQuantum System Identification System Identification
Department of Information Engineering, The Chinese University of Hong Kong. · John A. Paulson School of Engineering and Applied Sciences, Harvard University. · California Institute of Technology
Neural Phase Correlation
Jun 16, 2026Cole ReynoldsQuantum System Identification Frequency-Domain Feature Learning
Weyl Labs
SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
Jun 11, 2026Yash Vardhan Tomar, Dheeraj PeddireddyReinforcement Learning Quantum System Identification
Purdue University West Lafayette, United States
Learning thermodynamic master equations for open quantum systems
Jun 2, 2025Peter Sentz, Stanley Nicholson, Yujin Cho +3Quantum System Identification Interpretable ML
Department of Data and Decision Sciences, Emory University, Atlanta, GA 30322 · 2Division of Applied Mathematics, Brown University, Providence, RI 02912 · 3Lawrence Livermore National Laboratory, Livermore, CA 94550

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Transformer-Based Time-Series Inference of Lindblad Dynamics in Open Quantum Systems

Julian Guam, Jianqing Liu

Department of Computer Science, North Carolina State University, Raleigh, NC

Quantum System Identification System Identification Time Series Transformers

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The Lindblad master equation is the standard framework for describing the non-unitary evolution of open quantum systems, where environmental interactions induce dissipation and decoherence. When both the system Hamiltonian and the dissipation rates are partially unknown or explicitly time-dependent, traditional analytical inversion and system-identification techniques become intractable. Recent works have demonstrated that Transformer-based models can infer unknown dissipation rates from observable time series, yet these approaches typically rely on hand-crafted statistical features under idealized and highly restricted conditions. Here we advance the paradigm by introducing a raw time-series Transformer that directly ingests the full trajectories of Pauli expectation values

\langleσ_x(t)\rangle

\langleσ_y(t)\rangle

, and

\langleσ_z(t)\rangle

, thereby fully exploiting the self-attention mechanism for temporal modeling. The architecture is further extended to jointly learn unknown Hamiltonian parameters, handle multiple dissipation channels, and operate robustly under realistic measurement noise. Across all tested scenarios the model achieves consistently high reconstruction accuracy while eliminating manual feature engineering. This provides a scalable, robust, and versatile framework for quantum environment sensing in realistic open quantum systems.

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