Structured filtering

Presenting Author: Christopher Granade, Microsoft Research
Contributing Author(s): Nathan Wiebe

A major challenge facing parameter estimation in physics, including cutting-edge techniques such as sequential Monte Carlo methods, stems from the inability of existing approaches to robustly deal with experiments that have multiple equally plausible explanations. We address this problem by proposing a form of particle filtering that clusters the hypotheses that comprise the sequential Monte Carlo approximation before applying a resampler, allowing better approximations of posterior distributions. Through a new graphical approach to thinking about such models, we are able to devise an artificial intelligence–based strategy that automatically learns the shape and number of the clusters in the support of the posterior. We demonstrate the power of our approach by applying it to randomized gap estimation and a form of low circuit-depth phase estimation where existing methods from the physics literature either exhibit much worse performance or even fail completely.

Read this article online: http://iopscience.iop.org/article/10.1088/1367-2630/aa77cf/meta

(Session 5 : Thursday from 5:00pm - 7:00 pm)


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