Journal
NEW JOURNAL OF PHYSICS
Volume 14, Issue -, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1367-2630/14/10/103013
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Funding
- Canadian government through NSERC
- Canadian government through CERC
- United States government through DARPA
- USARO-DTO
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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.
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