Simulating the Photon Statistics of Multimode Gaussian States by Automatic Differentiation of Generating Functions

TL;DR

This paper introduces a versatile method for simulating photon statistics of multimode Gaussian states using automatic differentiation of generating functions, facilitating realistic quantum optical experiment analysis.

Contribution

The authors develop a novel approach employing automatic differentiation of generating functions to efficiently simulate photon statistics of Gaussian states, including photon-added and photon-subtracted variants.

Findings

Accurate simulation of photon number distributions and moments.

Application to quantum key distribution detection probabilities.

Method effectively incorporates experimental imperfections.

Abstract

Advances in photonics require photon-number resolved simulations of quantum optical experiments with Gaussian states. We demonstrate a simple and versatile method to simulate the photon statistics of general multimode Gaussian states. The derived generating functions enable simulations of the photon number distribution, cumulative probabilities, moments, and factorial moments of the photon statistics of Gaussian states as well as of multimode photon-added and photon-subtracted Gaussian states. Numerical results are obtained by automatic differentiation of these generating functions by employing the software framework PyTorch. Our approach is particularly well suited for practical simulations of the photon statistics of quantum optical experiments in realistic scenarios with low photon numbers, in which various sources of imperfections have to be taken into account. As an example, we calculate the detection probabilities for a recent multipartite time-bin coding quantum key distribution setup and compare them with the corresponding experimental values.