Robust Online Hamiltonian Learning
This work addresses the challenge of robust Hamiltonian learning in quantum systems, offering a practical solution for experimentalists, though it is incremental as it builds on existing methodologies.
The authors tackled the problem of inferring dynamical parameters of a quantum system by combining sequential Monte Carlo and Bayesian experimental design into an online algorithm that learns Hamiltonian parameters even with changing parameters and unknown noise, achieving performance certification via Cramer-Rao lower bound estimation.
In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.