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Uncertainty decomposition of quantum networks in SLH framework
Author(s) -
Azodi Peyman,
Setoodeh Peyman,
Khayatian Alireza,
Asemani Mohammad Hassan
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4740
Subject(s) - decomposition , quantum , representation (politics) , computer science , stability (learning theory) , quantum state , state space , mathematics , state (computer science) , mathematical optimization , topology (electrical circuits) , algorithm , physics , quantum mechanics , ecology , statistics , combinatorics , machine learning , politics , political science , law , biology
Summary This paper presents a systematic method to decompose uncertain linear quantum input‐output networks into uncertain and nominal subnetworks, when uncertainties are defined in SLH representation. To this aim, two decomposition theorems are stated, which show how an uncertain quantum network can be decomposed into nominal and uncertain subnetworks in cascaded connection and how uncertainties can be translated from SLH parameters into state‐space parameters. As a potential application of the proposed decomposition scheme, robust stability analysis of uncertain quantum networks is briefly introduced. The proposed uncertainty decomposition theorems take account of uncertainties in all three parameters of a quantum network and bridge the gap between SLH modeling and state‐space robust analysis theory for linear quantum networks.