English

Inferring causal structure: a quantum advantage

Quantum Physics 2015-06-08 v1 Machine Learning General Relativity and Quantum Cosmology Machine Learning

Abstract

The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines. Yet given correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both, unless one can implement a randomized intervention. We here consider the problem of causal inference for quantum variables. We introduce causal tomography, which unifies and generalizes conventional quantum tomography schemes to provide a complete solution to the causal inference problem using a quantum analogue of a randomized trial. We furthermore show that, in contrast to the classical case, observed quantum correlations alone can sometimes provide a solution. We implement a quantum-optical experiment that allows us to control the causal relation between two optical modes, and two measurement schemes -- one with and one without randomization -- that extract this relation from the observed correlations. Our results show that entanglement and coherence, known to be central to quantum information processing, also provide a quantum advantage for causal inference.

Keywords

Cite

@article{arxiv.1406.5036,
  title  = {Inferring causal structure: a quantum advantage},
  author = {Katja Ried and Megan Agnew and Lydia Vermeyden and Dominik Janzing and Robert W. Spekkens and Kevin J. Resch},
  journal= {arXiv preprint arXiv:1406.5036},
  year   = {2015}
}

Comments

17 pages, 6 figures. Comments welcome

R2 v1 2026-06-22T04:42:19.693Z