{"id":165418,"date":"2018-11-06T17:23:15","date_gmt":"2018-11-07T01:23:15","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/robust-online-hamiltonian-learning\/"},"modified":"2018-11-06T17:23:15","modified_gmt":"2018-11-07T01:23:15","slug":"robust-online-hamiltonian-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/robust-online-hamiltonian-learning\/","title":{"rendered":"Robust online Hamiltonian learning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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\u2013Rao lower bound, certifying its own performance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 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