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Quantum causal modelling

2015/12/31 by Fabio Costa, Sally Shrapnel · 278 citations
Computer Science · Mathematics · Physics and Astronomy · #Causal model #Causal structure #Computer science #Formalism (music) #Mathematics #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum mechanics #Set (abstract data type) #Statistical physics #Theoretical computer science #quant-ph

paper · pdf · doi:10.1088/1367-2630/18/6/063032

published in New Journal of Physics 18(6), 063032 (IOP Publishing) · 15+3 pages. Published version

openalex publication_date 2016/06/24 · arxiv created 2016/06/27 · arxiv updated 2016/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

Abstract

Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces 'spooky' hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order.

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