Poster
in
Workshop: NeurIPS 2024 Workshop: Machine Learning and the Physical Sciences
Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements
Amit Rege · Kristopher Tucker · Conor Smith · Claire Monteleoni
Abstract:
We present a machine learning approach for Hamiltonian parameter estimation in quantum systems using weak measurement data. Our model combines an LSTM-based encoder with a physics-informed decoder, incorporating a trainable correction term for unmodeled dynamics. Evaluated in both supervised and unsupervised settings, the method demonstrates high accuracy and robustness against noise. This approach offers a scalable solution for quantum system identification, outperforming traditional methods in parameter estimation tasks.
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