Learning Hidden Structures in Open Quantum Dynamics

TL;DR

This paper presents a machine-learning method to uncover hidden algebraic structures in open quantum systems using limited measurement data, focusing on invariant subalgebras and decoherence-free subalgebras.

Contribution

It introduces a novel maximum-likelihood estimation framework that infers algebraic structures underlying quantum dynamics from multi-time measurement sequences.

Findings

Successfully identified nontrivial algebraic structures in synthetic models.

Demonstrated the approach on a waveguide quantum electrodynamics system.

Revealed hidden invariant subalgebras from measurement data.

Abstract

We introduce a machine-learning approach for identifying hidden structural features of open quantum dynamics under restricted experimental access. Unlike most existing data-driven methods which focus on detection or prediction of dynamical behavior, our framework targets the inference of invariant algebraic structures underlying the effective Markovian evolution. Measurement limitations, symmetries, and superselection rules are incorporated through a $*$-algebraic description of accessible observables. The learning problem is formulated as maximum-likelihood estimation from multi-time measurement sequences, where the algebraic type of an invariant subalgebra - articularly a decoherence-free subalgebra - is treated as a discrete structural hypothesis. The feasibility of the approach is illustrated on multiple synthetic models and a waveguide quantum electrodynamics system, where nontrivial intermediate algebraic structures are identified directly from measurement data.