Quantum System Identification

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Overview

Quantum system identification asks how to recover a quantum system’s Hamiltonian, noise processes, or open-system dynamics from measured outcomes over time. Methods include adaptive experiment selection, randomized Pauli measurements, Bayesian and neural inference, and physically constrained models such as Lindblad equations. The results support device calibration, noise characterization, and predictions of how quantum systems will evolve. A central challenge is balancing measurement cost and scalability with identifiability: limited or noisy data may not distinguish different physical models, even when an estimator is accurate.

Latest papers

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15 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. From Single Spins to Ensembles: A Calibrated Symbolic Regression Pipeline for NV Spectroscopy

    Oct 26, 2026Evan Zhang, Zhenghang Zhi, Zewei Wang +1Magnetic Resonance SpectroscopyModel Calibration

    School of Physics, UNSW, Sydney, Australia · School of Electrical Engineering and Telecommunications, UNSW · Cryo-X Tech Co., Shanghai, China +1

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

    Oct 5, 2026Julian Guam, Jianqing LiuQuantum System IdentificationSystem Identification

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

  3. Simulation-Based Quantum System Inference with Neural Posterior Estimation

    Sep 28, 2026Hang Zou, Anton Frisk Kockum, Martin Rahm +1Simulation-Based InferenceNeural 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

  4. Phase-cycled randomized benchmarking of quantum processors: recovering hidden classical noise correlations

    Sep 6, 2026Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Abdul Akbar Khan +1Quantum System Identification

    Fandaqah, Al Khobar, Saudi Arabia · Heidelberg University, Heidelberg, Germany · Argaam, Riyadh, Saudi Arabia +1

  5. A Spectral Identifiability Threshold for Dissipative Rate Recovery from Truncated Liouvillian Spectra

    Aug 29, 2026Yujun Ji, Somyajit ChakrabortyParameter IdentifiabilityQuantum System Identification

    Dulwich College Shanghai, Shanghai, China · Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

  6. Partial-Moment PINNs for Caldeira--Leggett Parameter Learning in Quantum Brownian Motion

    Aug 24, 2026Krishna BhatiaParameter EstimationQuantum System Identification

    QuantumAI Lab, Fractal Analytics, Mumbai, India

  7. Quantum Gaussian processes for prediction of channel observations

    Aug 19, 2026Jonas Jäger, Yaroslav Khmelnitskiy, Paolo Braccia +4Quantum Machine LearningQuantum System Identification

    Department of Computer Science and Institute of Applied Mathematics, University of British Columbia, Vancouver, V6T 1Z4 B.C., Canada · Stewart Blusson Quantum Matter Institute, Vancouver, V6T 1Z4 B.C., Canada · Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland. +6

  8. Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements

    Jul 25, 2026Taiqi Zhou, Weiyuan GongQuantum System IdentificationSystem 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

  9. Learning the structure of open quantum systems

    Jun 29, 2026Laura Lewis, Ewin Tang, John WrightQuantum System IdentificationQuantum Machine Learning

    UC Berkeley

  10. Optimal Ansatz-free Hamiltonian Learning In Situ

    Jun 17, 2026Taiqi Zhou, Weiyuan GongQuantum System IdentificationSystem 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

  11. Neural Phase Correlation

    Jun 16, 2026Cole ReynoldsQuantum System IdentificationFrequency-Domain Feature Learning

    Weyl Labs

  12. SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

    Jun 11, 2026Yash Vardhan Tomar, Dheeraj PeddireddyReinforcement LearningQuantum System Identification

    Purdue University West Lafayette, United States

  13. Reinforcement learning for quantum processes with memory

    Mar 26, 2026Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu +2Quantum RLRegret Minimization in RL

    Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore · Centre for Quantum Technologies, Nanyang Technological University, Singapore · QICI Quantum Information and Computation Initiative, School of Computing and Data Science, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China +2

  14. Lindbladian Learning with Neural Differential Equations

    Mar 8, 2026Timothy Heightman, Roman Aseguinolaza Gallo, Edward Jiang +3Quantum System Identification

    ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels, Barcelona, Spain · Quside Technologies SL, Carrer d’Esteve Terradas 1, 08860 Castelldefels, Barcelona, Spain · CIC nanoGUNE Consolider, Tolosa Hiribidea 76, E-20018 Donostia-San Sebastian, Spain +3

  15. Learning thermodynamic master equations for open quantum systems

    Jun 2, 2025Peter Sentz, Stanley Nicholson, Yujin Cho +3Quantum System IdentificationInterpretable 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