Optimized detector tomography for photon-number resolving detectors with hundreds of pixels

TL;DR

This paper introduces an optimized detector tomography model for large photon-number resolving detectors, significantly reducing computational resources while maintaining high accuracy in state reconstruction.

Contribution

The authors develop a modified tomography model that decreases optimization variables, enabling efficient characterization of detectors with hundreds of pixels.

Findings

Fidelity of reconstructed states remains above 99%.

Computational time is reduced by about 50%.

Detector tomography feasible on supercomputers with large memory for up to 340 pixels.

Abstract

Photon-number resolving detectors with hundreds of pixels are now readily available, while the characterization of these detectors using detector tomography is computationally intensive. Here, we present a modified detector tomography model that reduces the number of variables that need optimization. To evaluate the effectiveness and accuracy of our model, we reconstruct the photon number distribution of optical coherent and thermal states using the expectation-maximization-entropy algorithm. Our results indicate that the fidelity of the reconstructed states remains above 99%, and the second and third-order correlations agree well with the theoretical values for a mean number of photons up to 100. We also investigate the computational resources required for detector tomography and find out that our approach reduces the solving time by around a half compared to the standard detector tomography approach, and the required memory resources are the main obstacle for detector tomography of a large number of pixels. Our results suggest that detector tomography is viable on a supercomputer with 1~TB RAM for detectors with up to 340 pixels.