Bayesian recursive data pattern tomography

TL;DR

This paper introduces a Bayesian recursive algorithm for quantum state reconstruction that efficiently predicts optimal measurement settings, reducing the number of measurements needed for accurate quantum state estimation.

Contribution

The paper proposes a novel Bayesian recursive method for data-pattern quantum tomography that optimizes measurement settings to improve efficiency and accuracy.

Findings

Reduces the number of measurements needed for accurate state reconstruction.

Enhances numerical efficiency of data-pattern tomography.

Predicts optimal measurement settings using a Bayesian approach.

Abstract

We present a simple and efficient Bayesian recursive algorithm for the data-pattern scheme for quantum state reconstruction, which is applicable to situations where measurement settings can be controllably varied efficiently. The algorithm predicts the best measurements required to accurately reconstruct the unknown signal state in terms of a fixed set of probe states. In each iterative step, this algorithm seeks the measurement setting that minimizes the variance of the data-pattern estimator, which essentially measures the reconstruction accuracy, with the help of a data-pattern bank that was acquired prior to the signal reconstruction. We show that with this algorithm, it is possible to minimize the number of measurement settings required to obtain a reasonably accurate state estimator by using just the optimal settings and, at the same time, increasing the numerical efficiency of the data-pattern reconstruction.