Statistically efficient tomography of low rank states with incomplete measurements
Anirudh Acharya, Theodore Kypraios, Madalin Guta

TL;DR
This paper demonstrates that low rank quantum states can be efficiently estimated with fewer measurement settings in tomography, maintaining accuracy through statistical analysis and simulations, especially for large systems.
Contribution
It introduces a method to reduce measurement settings in quantum tomography for low rank states, supported by theoretical concentration inequalities and extensive numerical simulations.
Findings
Error remains robust despite fewer measurement settings.
Significant reduction in settings with negligible error increase.
Measurement in O(r log d) bases suffices for certain states.
Abstract
The construction of physically relevant low dimensional state models, and the design of appropriate measurements are key issues in tackling quantum state tomography for large dimensional systems. We consider the statistical problem of estimating low rank states in the set-up of multiple ions tomography, and investigate how the estimation error behaves with a reduction in the number of measurement settings, compared with the standard ion tomography setup. We present extensive simulation results showing that the error is robust with respect to the choice of states of a given rank, the random selection of settings, and that the number of settings can be significantly reduced with only a negligible increase in error. We present an argument to explain these findings based on a concentration inequality for the Fisher information matrix. In the more general setup of random basis measurements…
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