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This research presents a novel machine-learning framework designed to infer invariant algebraic structures within open quantum systems, specifically addressing challenges posed by restricted experimental access and measurement limitations. By formulating the learning problem as maximum-likelihood estimation from multi-time sequences, the method successfully identifies decoherence-free subalgebras in both synthetic models and waveguide QED setups without relying on full system state tomography. The work advances the intersection of data-driven inference with fundamental quantum dynamics, offering a pathway to extract hidden symmetries directly from noisy observational data.
Learning Hidden Structures in Open Quantum Dynamics
arXiv:2604.00711v2 Announce Type: replace 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, particularly 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.
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, particularly 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.
arXiv:2604.00711 [quant-ph]
(or arXiv:2604.00711v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2604.00711 Focus to learn more arXiv-issued DOI via DataCite
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