Efficient algorithm for optimizing data pattern tomography

TL;DR

This paper presents an efficient algorithm for data pattern tomography that reconstructs quantum states without prior calibration, particularly useful for complex detectors, demonstrated through homodyne detection of a nonclassical photon state.

Contribution

The paper introduces a novel, efficient search algorithm for data pattern tomography, improving state reconstruction in complex measurement scenarios.

Findings

Algorithm effectively reconstructs quantum states without prior calibration.

Demonstrated success in homodyne detection of nonclassical photon states.

Applicable to complex, hard-to-characterize detectors.

Abstract

We give a detailed account of an efficient search algorithm for the data pattern tomography proposed by J. Rehacek, D. Mogilevtsev, and Z. Hradil [Phys. Rev. Lett.~\textbf{105}, 010402 (2010)], where the quantum state of a system is reconstructed without a priori knowledge about the measuring setup. The method is especially suited for experiments involving complex detectors, which are difficult to calibrate and characterize. We illustrate the approach with the case study of the homodyne detection of a nonclassical photon state.