Experimental nonclassicality in a causal network without assuming freedom of choice

TL;DR

This paper demonstrates nonclassical correlations in a photonic triangle causal network without relying on free external inputs, using advanced data analysis techniques to identify quantum effects.

Contribution

It provides the first experimental realization of nonclassicality in a triangle causal network without assuming free choice, employing novel data analysis methods.

Findings

Successful demonstration of nonclassical correlations in a triangle network

Adaptation of machine learning and inflation techniques for data analysis

Broad applicability of the methods for complex quantum networks

Abstract

In a Bell experiment, it is natural to seek a causal account of correlations wherein only a common cause acts on the outcomes. For this causal structure, Bell inequality violations can be explained only if causal dependencies are modelled as intrinsically quantum. There also exists a vast landscape of causal structures beyond Bell that can witness nonclassicality, in some cases without even requiring free external inputs. Here, we undertake a photonic experiment realizing one such example: the triangle causal network, consisting of three measurement stations pairwise connected by common causes and no external inputs. To demonstrate the nonclassicality of the data, we adapt and improve three known techniques: (i) a machine-learning-based heuristic test, (ii) a data-seeded inflation technique generating polynomial Bell-type inequalities and (iii) entropic inequalities. The demonstrated experimental and data analysis tools are broadly applicable paving the way for future networks of growing complexity.