@article{granade2012robust,
author = {Granade, Chris and Ferrie, Chris and Wiebe, Nathan and Cory, David},
title = {Robust online Hamiltonian learning},
year = {2012},
month = {January},
abstract = {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.},
publisher = {IOP},
url = {http://approjects.co.za/?big=en-us/research/publication/robust-online-hamiltonian-learning/},
journal = {New Journal of Physics},
}