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A categorical semantics for causal structure

Abstract:
We present a categorical construction for modelling causal structures within a general class of process theories that include the theory of classical probabilistic processes as well as quantum theory. Unlike prior constructions within categorical quantum mechanics, the objects of this theory encode fine-grained causal relationships between subsystems and give a new method for expressing and deriving consequences for a broad class of causal structures. We show that this framework enables one to define families of processes which are consistent with arbitrary acyclic causal orderings. In particular, one can define one-way signalling (a.k.a. semi-causal) processes, non-signalling processes, and quantum 𝑛-combs. Furthermore, our framework is general enough to accommodate recently-proposed generalisations of classical and quantum theory where processes only need to have a fixed causal ordering locally, but globally allow indefinite causal ordering. To illustrate this point, we show that certain processes of this kind, such as the quantum switch, the process matrices of Oreshkov, Costa, and Brukner, and a classical three-party example due to Baumeler, Feix, and Wolf are all instances of a certain family of processes we refer to as SOC𝑛 in the appropriate category of higher-order causal processes. After defining these families of causal structures within our framework, we give derivations of their operational behaviour using simple, diagrammatic axioms.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.23638/LMCS-15(3:15)2019

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Hilda's College
Role:
Author


Publisher:
Logical Methods in Computer Science
Journal:
Logical Methods in Computer Science More from this journal
Volume:
15
Issue:
3
Article number:
15
Publication date:
2019-08-09
DOI:
ISSN:
1860-5974


Language:
English
Keywords:
Pubs id:
1056740
Local pid:
pubs:1056740
Deposit date:
2020-10-21

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