Inferring causal structure: a quantum advantage

TL;DR

This paper introduces quantum causal tomography and demonstrates that quantum correlations can sometimes infer causal relations without interventions, showcasing a quantum advantage in causal inference.

Contribution

The paper presents a novel quantum causal inference method and experimentally shows quantum correlations can determine causality without randomized trials.

Findings

Quantum correlations can infer causality without interventions.

Quantum causal tomography unifies and generalizes existing quantum tomography.

Entanglement and coherence enable a quantum advantage in causal inference.

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.