Adaptive Hamiltonian Estimation Using Bayesian Experimental Design
Abstract
Using Bayesian experimental design techniques, we have shown that for a single two-level quantum mechanical system under strong (projective) measurement, the dynamical parameters of a model Hamiltonian can be estimated with exponentially improved accuracy over offline estimation strategies. To achieve this, we derive an adaptive protocol which finds the optimal experiments based on previous observations. We show that the risk associated with this algorithm is close to the global optimum, given a uniform prior. Additionally, we show that sampling at the Nyquist rate is not optimal.
Cite
@article{arxiv.1111.0935,
title = {Adaptive Hamiltonian Estimation Using Bayesian Experimental Design},
author = {Christopher Ferrie and Christopher E. Granade and D. G. Cory},
journal= {arXiv preprint arXiv:1111.0935},
year = {2012}
}
Comments
8 pages, 3 figures. To appear in Bayesian Inference And Maximum Entropy Methods In Science And Engineering: Proceedings of the 31th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering