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

TL;DR

This paper introduces a novel system identification algorithm for linear quantum systems driven by time-varying inputs, using homodyne measurement data to produce physically realizable models, demonstrated on optical cavity examples.

Contribution

It presents the first data-driven method for identifying linear quantum systems with time-varying inputs that ensures physical realizability constraints are met.

Findings

Successfully identified models for optical cavity systems

Algorithm handles time-varying coherent inputs

Models satisfy quantum physical constraints

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.