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Conference Papers Year : 2020

Data-Driven System Identification of Linear Quantum Systems Coupled to Time-Varying Coherent Inputs

Abstract

In this paper, we develop a system identification algorithm to identify a model for unknown linear quantum systems driven by time-varying coherent states, based on empirical single-shot continuous homodyne measurement data of the system's output. The proposed algorithm identifies a model that satisfies the physical realizability conditions for linear quantum systems, challenging constraints not encountered in classical (non-quantum) linear system identification. Numerical examples on a multiple-input multiple-output optical cavity model are presented to illustrate an application of the identification algorithm.
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Dates and versions

hal-03059873 , version 1 (25-11-2021)

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Hendra I. Nurdin, Nina Amini, Jiayin Chen. Data-Driven System Identification of Linear Quantum Systems Coupled to Time-Varying Coherent Inputs. 2020 59th IEEE Conference on Decision and Control (CDC), Dec 2020, Jeju, South Korea. ⟨10.1109/CDC42340.2020.9303815⟩. ⟨hal-03059873⟩
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