Quantum Ensemble Classification: A Sampling-Based Learning Control Approach

Chunlin Chen, Daoyi Dong, Bo Qi, Ian R. Petersen, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Article number7439835
Pages (from-to)1345-1359
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number6
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


  • Inhomogeneous ensembles
  • quantum discrimination
  • quantum ensemble classification (QEC)
  • sampling-based learning control (SLC)


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