Coarse-grained quantum state estimation for noisy measurements

TL;DR

This paper presents a simple numerical coarse-graining method for estimating quantum states from noisy measurement data, improving efficiency without requiring detailed calibration or assumptions.

Contribution

It introduces a practical, entropy-based coarse-graining scheme applicable to noisy quantum measurements, enhancing tomography efficiency in experimental setups.

Findings

Coarse-graining improves quantum state estimation accuracy.

Method is effective across various noise levels.

Applicable to general noisy measurement scenarios.

Abstract

We introduce a straightforward numerical coarse-graining scheme to estimate quantum states for a set of noisy measurement outcomes, which are difficult to calibrate, that is based solely on the measurement data collected from these outcomes. This scheme involves the maximization of a weighted entropy function that is simple to implement and can readily be extended to any number of ill-calibrated noisy outcomes in a measurement set-up, thus offering practical applicability for general tomography experiments without additional knowledge or assumptions about the structures of the noisy outcomes. Simulation results for two-qubit quantum states show that coarse-graining can improve the tomographic efficiencies for noise levels ranging from low to moderately high values.