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Open Quantum Systems Decoherence Quantum Machine Learning

Tangential Interpolatory Projection for Model Reduction of Linear Quantum Stochastic Systems

arXiv
Authors: O. Techakesari, H. I. Nurdin

Year

2015

Paper ID

27177

Status

Preprint

Abstract Read

~2 min

Abstract Words

125

Citations

1

Abstract

This paper presents a model reduction method for the class of linear quantum stochastic systems often encountered in quantum optics and their related fields. The approach is proposed on the basis of an interpolatory projection ensuring that specific input-output responses of the original and the reduced-order systems are matched at multiple selected points (or frequencies). Importantly, the physical realizability property of the original quantum system imposed by the law of quantum mechanics is preserved under our tangential interpolatory projection. An error bound is established for the proposed model reduction method and an avenue to select interpolation points is proposed. A passivity preserving model reduction method is also presented. Examples of both active and passive systems are provided to illustrate the merits of our proposed approach.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2015 reference point for readers tracking recent quantum research.
  • This paper presents a model reduction method for the class of linear quantum stochastic systems often encountered in quantum optics and their related fields.

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