Systems identification for passive linear quantum systems: The transfer function approach

Madalin Guta, Naoki Yamamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


System identification is a key enabling component for the implementation of new quantum technologies, including quantum control. In this paper we consider a large class of input-output systems, namely linear passive quantum systems, and study the following identifiability question: if the system's Hamiltonian and coupling matrices are unknown, which of these dynamical parameters can be estimated by preparing appropriate input states and performing measurements on the output? The input-output mapping is explicitly given by the transfer function, which contains the maximum information about the system.We show that two minimal systems are indistinguishable (have the same transfer function) if and only if their Hamiltonians and the coupling to the input fields are related by a unitary transformation. Furthermore, we provide a canonical parametrization of the equivalence classes of indistinguishable systems. For models depending on (possibly lower dimensional) unknown parameters, we give a practical identifiability condition which is illustrated on several examples. In particular, we show that systems satisfying a certain Hamiltonian connectivity condition called "infecting", are completely identifiable.

Original languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781467357173
Publication statusPublished - 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: 2013 Dec 102013 Dec 13

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other52nd IEEE Conference on Decision and Control, CDC 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization


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