Quantum System Identification

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

CardsList

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

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

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

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

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

  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

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

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

  6. Quantum Gaussian processes for prediction of channel observations

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

  7. Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements

    Jul 25, 2026Taiqi Zhou, Weiyuan GongQuantum System IdentificationSystem Identification

  8. Optimal Ansatz-free Hamiltonian Learning In Situ

    Jun 17, 2026Taiqi Zhou, Weiyuan GongQuantum System IdentificationSystem Identification

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

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

  10. Reinforcement learning for quantum processes with memory

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

  11. Lindbladian Learning with Neural Differential Equations

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

  12. Learning thermodynamic master equations for open quantum systems

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