Identification and Estimation of Quantum Linear Input-Output Systems

Abstract

The system identification problem is to estimate dynamical parameters from the output data, obtained by performing measurements on the output fields. We investigate system identification for quantum linear systems. Our main objectives are to address the following general problems: (1) Which parameters can be identified by measuring the output? (2) How can we construct a system realisation from sufficient input-output data? (3) How well can we estimate the parameters governing the dynamics? We investigate these problems in two contrasting approaches; using time-dependent inputs or time-stationary (quantum noise) inputs.

Matthew Levitt PhD Thesis

References in corpus (16)

• The elusive Heisenberg limit in quantum enhanced metrology
• Cavity Loss Induced Generation of Entangled Atoms
• Quantum Enhanced Multiple Phase Estimation
• Quantum Fisher information for states in exponential form
• Time-delayed quantum feedback control
• Phase space formalism for quantum estimation of Gaussian states
• The Kalman Decomposition for Linear Quantum Systems
• Theory of transformation for the diagonalization of quadratic Hamiltonians
• Decoherence-free linear quantum subsystems
• Information geometry and local asymptotic normality for multi-parameter estimation of quantum Markov dynamics
• Power Spectrum Identification for Quantum Linear Systems
• Networked quantum sensing
• Feedback Network Models for Quantum Transport
• Modular Quantum Memories Using Passive Linear Optics and Coherent Feedback
• Protected Subspaces in Quantum Information
• Dynamical analysis of quantum linear systems driven by multi-channel multi-photon states