Back to Search Start Over

Hamiltonian Identification via Quantum Ensemble Classification

Authors :
Yu, Haixu
Zhao, Xudong
Dong, Daoyi
Chen, Chunlin
Source :
IEEE Transactions on Neural Networks and Learning Systems; August 2024, Vol. 35 Issue: 8 p11261-11275, 15p
Publication Year :
2024

Abstract

Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.

Details

Language :
English
ISSN :
2162237x and 21622388
Volume :
35
Issue :
8
Database :
Supplemental Index
Journal :
IEEE Transactions on Neural Networks and Learning Systems
Publication Type :
Periodical
Accession number :
ejs67130339
Full Text :
https://doi.org/10.1109/TNNLS.2023.3258622