Experimental statistical signature of many-body quantum interference

TL;DR

This paper demonstrates an experimental method to detect many-body quantum interference using statistical signatures, validated on three-photon integrated photonic circuits, and enhanced by machine learning techniques for feature identification.

Contribution

It introduces an efficient protocol for witnessing many-body quantum interference and applies machine learning to optimize the detection process in quantum photonic experiments.

Findings

Successful validation on three-photon experiments

Identification of optimal features using machine learning

Evidence supporting the method's efficacy and scalability

Abstract

Multi-particle interference is an essential ingredient for fundamental quantum mechanics phenomena and for quantum information processing to provide a computational advantage, as recently emphasized by Boson Sampling experiments. Hence, developing a reliable and efficient technique to witness its presence is pivotal towards the practical implementation of quantum technologies. Here we experimentally identify genuine many-body quantum interference via a recent efficient protocol, which exploits statistical signatures at the output of a multimode quantum device. We successfully apply the test to validate three-photon experiments in an integrated photonic circuit, providing an extensive analysis on the resources required to perform it. Moreover, drawing upon established techniques of machine learning, we show how such tools help to identify the - a priori unknown - optimal features to witness these signatures. Our results provide evidence on the efficacy and feasibility of the method, paving the way for its adoption in large-scale implementations.