Experimental nonclassicality in a causal network without assuming freedom of choice

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.

Version published in Nature Communications

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Cited by in corpus (11)

• Certification of non-classicality in all links of a photonic star network without assuming quantum mechanics
• Entanglement Verification with Deep Semi-supervised Machine Learning
• Proposals for ruling out the real quantum theories in an entanglement-swapping quantum network with causally independent sources
• Noise-robust proofs of quantum network nonlocality
• Relating Wigner's Friend Scenarios to Nonclassical Causal Compatibility, Monogamy Relations, and Fine Tuning
• Memory attacks in network nonlocality and self-testing
• Closing the detection loophole in the triangle network with high-dimensional photonic states
• Experimental genuine quantum nonlocality in the triangle network
• Exploring the Local Landscape in the Triangle Network
• Quantum non-classicality in the simplest causal network
• Fully quantum inflation: quantum marginal problem constraints in the service of causal inference