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This research presents a novel unsupervised autoencoder framework that directly infers effective Hamiltonians from experimental transport data in solid-state quantum systems. By embedding S-matrix formalism into the decoder, the model achieves physically meaningful parameter identification without requiring labeled training sets, demonstrating robustness against noise and generalization beyond the initial training domain. This advancement significantly aids the automated characterization of programmable solid-state simulators, bridging the gap between raw experimental measurements and quantum system modeling.
Learning Hamiltonians for solid-state quantum simulators
arXiv:2603.02889v2 Announce Type: replace-cross Abstract: We introduce a generalizable framework for identifying effective Hamiltonians directly from experimental data in solid-state quantum systems. Our unsupervised autoencoder-based approach incorporates the governing physics (here, the S-matrix formalism) directly into the decoder, enabling physically meaningful Hamiltonian inference without labeled Hamiltonian parameters. Through numerical experiments on a triple quantum dot chain, we demonstrate automated characterization of programmable solid-state simulators from transport measurements. The model accurately infers Hamiltonian parameters and generalizes beyond the training domain, while exhibiting finite capacity as the parameter space becomes increasingly broad or diverse. We further demonstrate robustness to noisy measurements and show that the model can be successfully trained on transport data generated outside the Hamiltonian family assumed by the physics decoder, while still identifying meaningful effective parameters.
Abstract:We introduce a generalizable framework for identifying effective Hamiltonians directly from experimental data in solid-state quantum systems. Our unsupervised autoencoder-based approach incorporates the governing physics (here, the S-matrix formalism) directly into the decoder, enabling physically meaningful Hamiltonian inference without labeled Hamiltonian parameters. Through numerical experiments on a triple quantum dot chain, we demonstrate automated characterization of programmable solid-state simulators from transport measurements. The model accurately infers Hamiltonian parameters and generalizes beyond the training domain, while exhibiting finite capacity as the parameter space becomes increasingly broad or diverse. We further demonstrate robustness to noisy measurements and show that the model can be successfully trained on transport data generated outside the Hamiltonian family assumed by the physics decoder, while still identifying meaningful effective parameters.
Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Disordered Systems and Neural Networks (cond-mat.dis-nn); Quantum Physics (quant-ph)
arXiv:2603.02889 [cond-mat.mes-hall]
(or arXiv:2603.02889v2 [cond-mat.mes-hall] for this version)
https://doi.org/10.48550/arXiv.2603.02889 Focus to learn more arXiv-issued DOI via DataCite
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