TY - JOUR
T1 - Quantum Ensemble Classification
T2 - A Sampling-Based Learning Control Approach
AU - Chen, Chunlin
AU - Dong, Daoyi
AU - Qi, Bo
AU - Petersen, Ian R.
AU - Rabitz, Herschel
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61004049, Grant 61273327, and Grant 61374092, in part by the Australian Research Council under Grant DP130101658 and Grant FL110100020, and in part by the U.S. National Science Foundation under Grant CHE-0718610.
Publisher Copyright:
© 2016 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.
AB - Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.
KW - Inhomogeneous ensembles
KW - quantum discrimination
KW - quantum ensemble classification (QEC)
KW - sampling-based learning control (SLC)
UR - http://www.scopus.com/inward/record.url?scp=84961938209&partnerID=8YFLogxK
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U2 - 10.1109/TNNLS.2016.2540719
DO - 10.1109/TNNLS.2016.2540719
M3 - Article
C2 - 28113872
AN - SCOPUS:84961938209
SN - 2162-237X
VL - 28
SP - 1345
EP - 1359
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 6
M1 - 7439835
ER -