Information geometry and local asymptotic normality for multi-parameter estimation of quantum Markov dynamics

TL;DR

This paper develops an information geometric framework for multi-parameter estimation of quantum Markov dynamics, characterizing identifiable parameters, defining a quantum Fisher information metric, and establishing local asymptotic normality for system-output states.

Contribution

It introduces a novel geometric structure on the parameter space, including a principal bundle, connection, and metric, and demonstrates local asymptotic normality in quantum Markov systems.

Findings

Characterization of identifiable parameters as Lie group orbits.

Explicit computation of the quantum Fisher information metric.

Proof of local asymptotic normality for system-output states.

Abstract

This paper deals with the problem of identifying and estimating dynamical parameters of continuous-time quantum open systems, in the input-output formalism. First, we characterise the space of identifiable parameters for ergodic dynamics, assuming full access to the output state for arbitrarily long times, and show that the equivalence classes of undistinguishable parameters are orbits of a Lie group acting on the space of dynamical parameters. Second, we define an information geometric structure on this space, including a principal bundle given by the action of the group, as well as a compatible connection, and a Riemannian metric based on the quantum Fisher information of the output. We compute the metric explicitly in terms of the Markov covariance of certain "fluctuation operators", and relate it to the horizontal bundle of the connection. Third, we show that the system-output and reduced output state satisfy local asymptotic normality, i.e. they can be approximated by a Gaussian model consisting of coherent states of a multimode continuos variables system constructed from the Markov covariance "data". We illustrate the result by working out the details of the information geometry of a physically relevant two-level system.