Experimental nonclassicality in a causal network without assuming freedom of choice.

• DOI: 10.48550/arxiv.2210.07263
• Publication Date: 2023-02-17
• Authors: Emanuele Polino; Davide Poderini; Giovanni Rodari; Iris Agresti; Alessia Suprano; Gonzalo Carvacho; Elie Wolfe; Askery Canabarro; George Moreno; Giorgio Milani; Robert W. Spekkens; Rafael Chaves; Fabio Sciarrino
• Keywords: Artificial intelligence; Science; Foundations of Quantum Mechanics and Interpretations; FOS: Physical sciences; Heuristic; Pairwise comparison; Quantum mechanics; 01 natural sciences; Article; Quantum; Causal model; Quantum entanglement; Theoretical computer science; Artificial Intelligence; Causal structure; 0103 physical sciences; Nonequilibrium Systems; FOS: Mathematics; Stochastic Thermodynamics and Fluctuation Theorems; Quantum Physics; Physics; Q; Statistics; Statistical and Nonlinear Physics; Degrees of freedom (physics and chemistry); Computer science; Atomic and Molecular Physics, and Optics; Quantum Information and Computation; Physics and Astronomy; Bell's theorem; Causality (physics); quantum information; quantum nonclassicality; quantum network; Physical Sciences; Computer Science; Bell test experiments; Quantum Physics (quant-ph); Mathematics

AbstractIn 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 modeled 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.