Similarly, knowing the air is more polluted, I can infer that there are more cars. For example, compare the statistical statement “the number of cars is correlated with the amount of air pollution” and the causal statement “cars cause air pollution.” The statistical statement goes both ways: Knowing there are more cars, I can infer that the air is more polluted. Nowadays, however, it is recognized that the concept of causal information goes beyond that of correlation. Historically, statisticians thought that all information about a system could be represented in terms of statistical correlations among its variables.
John-Mark Allen at the University of Oxford, UK, and colleagues have now proposed a quantum causal model based on a generalization of an old principle known as Reichenbach’s common cause principle. For instance, if two or more quantum systems are entangled, it is hard to deduce whether statistical correlations between them imply a cause-effect relationship. Developing quantum extensions of causal models, however, has proven challenging because of the peculiar features of quantum mechanics. Can similar models be extended to the microscopic world governed by the laws of quantum mechanics? Answering this question could lead to advances in quantum information and to a better understanding of the foundations of quantum mechanics. Such models can be applied, for instance, to establish causal relationships between smoking and cancer or to analyze risks in construction projects. Mathematical models for deducing cause-effect relationships from statistical data have been successful in diverse areas of science (see Ref. have now proposed a quantum extension of causal models. Existing models, however, do not apply if at least one component of the system ( Ψ) is quantum. This will be achieved through the intensive collaboration between theory and experimental groups and the organization of a school and workshop fostering collaborations and inspiring future developments.APS/ Alan Stonebraker Figure 1: In statistics, causal models can be used to extract cause-effect relationships from empirical data on a complex system.
Basic questions include: What is required for the emergence of non-classicality in a causal structure? How can we turn this into a novel resource? What are the consequences to usual causal discovery algorithms? Answers to these questions will shed new light on the advantages and limitations of quantum enhanced protocols and thus unveil the role of causality in quantum theory. This project aims to bring this field to its next level, understanding theoretically and experimentally the foundational and practical implications of quantum causality. To cope with that, a new research field has emerged to develop the basic framework to reason about causality in a quantum world. Yet, the understanding of entanglement and non-locality -and its interconnections to how we perceive space and time and manipulate information- has been mostly restricted to simple causal structures. Both entanglement and non-locality are the core of the second quantum revolution as they enable the enhanced processing of quantum information in diverse applications.
Entangled quantum particles can be so strongly correlated that it seems like they behave as a single information unit, defying our most basic notions of space and time and leading to the the apparent "spooky action at a distance" abhorred by Einstein in the phenomenon known as quantum non-locality. To shake even more the foundations of causality, it has been discovered that our most basic notions of cause and effect are incompatible with quantum phenomena. At least those governed by classical mechanics. Yet, only recently a theory of causality was introduced, allowing to model causation in various fields of research. Explaining the natural world in terms of cause and effect is the central pillar of science.
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