PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adaptive Bayesian Quantum Tomography
Ferenc Huszar and Neil Houlsby
arXiv 2011.

Abstract

In this letter we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance of the experiment, we allow for measurements to be adaptively and efficiently re-optimised depending on data collected so far. We develop an adaptive statistical framework based on Bayesian inference and Shannon's information, and demonstrate a ten-fold reduction in the total number of measurements required as compared to non-adaptive methods, including mutually unbiased bases.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:8442
Deposited By:Ferenc Huszar
Deposited On:08 January 2012