Autoencoder infers Hamiltonians from quantum dot chains

Jarosław Pawłowski and Mateusz Krawczyk of Wrocław University of Science and Technology introduce a generalizable framework for identifying effective Hamiltonians directly from experimental data in solid-state quantum systems. Their unsupervised autoencoder-based approach incorporates the S-matrix formalism into the decoder, enabling physically meaningful Hamiltonian inference without labeled Hamiltonian parameters. Through numerical experiments on a triple quantum dot chain analyzed via transport measurements, the model accurately infers Hamiltonian parameters and demonstrates generalization beyond the training domain. Numerical experiments demonstrated automated characterization of programmable solid-state quantum simulators using a novel autoencoder approach and transport measurements performed on a triple quantum dot chain. The model accurately infers Hamiltonian parameters, a feat achieved without requiring labeled data for training, and exhibits generalization capabilities beyond the initially defined training domain. The framework’s robustness was confirmed through tests involving noisy measurements. The autoencoder successfully predicted Hamiltonian parameters even from conductance maps containing disturbances.…