@inproceedings{49d0674c82f949a1bb7adb3531b4845d,
title = "Hamiltonian Identification of Quantum Networks from Sequential Boolean Measurements",
abstract = "Hamiltonian identification plays a key role in learning the dynamics of closed quantum systems. A novel strategy for Hamiltonian identification is developed in the paper by applying repeated projective measurements with a fixed period. The corresponding measurement outcome sequence forms a time-homogeneous Markov chain, the transition matrix of which is a quadratic function of the Hamiltonian. As a result, the identification method is achieved by two steps: 1) learning the transition matrix of the observed Markov chains; 2) identifying the Hamiltonian using the function relating it to the transition matrix, which is transformed to a manifold optimization problem. A simulation example is provided to show the efficacy of the proposed method.",
keywords = "Hamiltonian identification, repeated projective measurements, transition matrix learning",
author = "Biqiang Mu and Hongsheng Qi and Petersen, \{Ian R.\} and Guodong Shi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 59th IEEE Conference on Decision and Control, CDC 2020 ; Conference date: 14-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/CDC42340.2020.9304353",
language = "English",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1008--1013",
booktitle = "2020 59th IEEE Conference on Decision and Control, CDC 2020",
address = "United States",
}