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Open Quantum Systems Decoherence Quantum Machine Learning

Uncertainty decomposition of quantum networks in SLH framework

arXiv
Authors: Peyman Azodi, Alireza Khayatian, Peyman Setoodeh, Mohammad Hassan Asemani

Year

2016

Paper ID

43352

Status

Preprint

Abstract Read

~2 min

Abstract Words

115

Citations

N/A

Abstract

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.

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  • This paper presents a systematic method to decompose uncertain linear quantum input-output networks into uncertain and nominal subnetworks, when uncertainties are defined in...

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