Learning Hamiltonians for solid-state quantum simulators
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910039236673536 |
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| author | Pawłowski, Jarosław Krawczyk, Mateusz |
| author_facet | Pawłowski, Jarosław Krawczyk, Mateusz |
| contents | We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure. Unlike purely data-driven supervised schemes, the proposed unsupervised autoencoder-based method incorporates the governing physics (here, the S-matrix formalism) within the decoder network, ensuring that the learned representations remain physically meaningful. Through numerical learning experiments, we demonstrate automated characterization of programmable solid-state simulators from transport measurements, exemplified by a triple quantum dot chain. The trained model generalizes beyond the training domain and accurately infers Hamiltonian parameters from transport data. While the model has finite capacity -- leading to degraded performance when the parameter space becomes excessively large or structurally diverse -- we identify regimes in which robust generalization is maintained. We further show how to train the model to handle noisy measurements, reflecting realistic experimental conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Hamiltonians for solid-state quantum simulators Pawłowski, Jarosław Krawczyk, Mateusz Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Quantum Physics We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure. Unlike purely data-driven supervised schemes, the proposed unsupervised autoencoder-based method incorporates the governing physics (here, the S-matrix formalism) within the decoder network, ensuring that the learned representations remain physically meaningful. Through numerical learning experiments, we demonstrate automated characterization of programmable solid-state simulators from transport measurements, exemplified by a triple quantum dot chain. The trained model generalizes beyond the training domain and accurately infers Hamiltonian parameters from transport data. While the model has finite capacity -- leading to degraded performance when the parameter space becomes excessively large or structurally diverse -- we identify regimes in which robust generalization is maintained. We further show how to train the model to handle noisy measurements, reflecting realistic experimental conditions. |
| title | Learning Hamiltonians for solid-state quantum simulators |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Quantum Physics |
| url | https://arxiv.org/abs/2603.02889 |