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The notions of causality adopted within the quantum information and spacetime physics communities are distinct. Although both notions play a role in physical experiments, their general interplay is little understood in theory. We develop a…

Quantum Physics · Physics 2024-10-08 V. Vilasini , Renato Renner

In distributed systems where strong consistency is costly when not impossible, causal consistency provides a valuable abstraction to represent program executions as partial orders. In addition to the sequential program order of each…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-03-15 Matthieu Perrin , Achour Mostefaoui , Claude Jard

In this "map" we are going to present the concept of indefinite causal order and make a quick journey through its different flavours. We will start with a broad conceptual motivation for studying indefinite causal order, based on the…

Quantum Physics · Physics 2025-06-06 Jorge Escandón-Monardes

Quantum processes with indefinite causal structure emerge when we wonder which are the most general evolutions, allowed by quantum theory, of a set of local systems which are not assumed to be in any particular causal order. These processes…

Quantum Physics · Physics 2024-02-07 Luca Apadula , Alessandro Bisio , Paolo Perinotti

What does it mean for a causal structure to be `unknown'? Can we even talk about `repetitions' of an experiment without prior knowledge of causal relations? And under what conditions can we say that a set of processes with arbitrary,…

Quantum Physics · Physics 2025-02-12 Fabio Costa , Jonathan Barrett , Sally Shrapnel

Bell nonlocality is one of the most intriguing and counter-intuitive phenomena displayed by quantum systems. Interestingly, such stronger-than-classical quantum correlations are somehow constrained, and one important question to the…

Quantum Physics · Physics 2023-04-13 Lucas Pollyceno , Rafael Chaves , Rafael Rabelo

It has been shown that it is theoretically possible for there to exist higher-order quantum processes in which the operations performed by separate parties cannot be ascribed a definite causal order. Some of these processes are believed to…

Quantum Physics · Physics 2019-12-04 Ognyan Oreshkov

Spatial separation restricts the set of locally implementable quantum operations on distributed multipartite quantum systems. We propose that indefinite causal structure arising due to quantum superposition of different space-time…

Quantum Physics · Physics 2023-02-21 Pratik Ghosal , Arkaprabha Ghosal , Debarshi Das , Ananda G. Maity

Quantum coherence is a prime resource in quantum computing and quantum communication. Quantum coherence of an arbitrary qubit state can be created at a remote location using maximally entangled state, local operation and classical…

Quantum Physics · Physics 2023-02-09 Jasleen Kaur , Shrobona Bagchi , Arun Kumar Pati

In classical physics, events follow a definite causal order: the past influences the future, but not the reverse. Quantum theory, however, permits superpositions of causal orders -- so-called indefinite causal orders -- which can provide…

Quantum Physics · Physics 2026-04-29 Carla M. D. Richter , Michael Antesberger , Huan Cao , Philip Walther , Lee A. Rozema

Integrating deep learning and causal discovery has encouraged us to spot that learning causal structures and representations in dialogue and video is full of challenges. We defined These data forms as "Indefinite Data", characterized by…

Machine Learning · Computer Science 2024-01-17 Hang Chen , Xinyu Yang , Keqing Du

Recently, the possible existence of quantum processes with indefinite causal order has been extensively discussed, in particular using the formalism of process matrices. Here we give a new perspective on this question, by establishing a…

Modern machine learning (ML) methods typically fail to adequately capture causal information. Consequently, such models do not handle data distributional shifts, are vulnerable to adversarial examples, and often learn spurious correlations.…

Quantum Physics · Physics 2026-01-27 Rishi Goel , Casey R. Myers , Sally Shrapnel

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is…

Artificial Intelligence · Computer Science 2025-08-27 Alessio Zanga , Elif Ozkirimli , Fabio Stella

We give an operational definition of information-theoretic resources within a given multipartite classical or quantum correlation. We present our causal model that serves as the source coding side of this correlation and introduce a novel…

Information Theory · Computer Science 2025-07-11 Shuchan Wang , Gerhard Wunder

Requiring that the causal structure between different parties is well-defined imposes constraints on the correlations they can establish, which define so-called causal correlations. Some of these are known to have a "dynamical" causal order…

Quantum Physics · Physics 2025-11-13 Raphaël Mothe , Alastair A. Abbott , Cyril Branciard

In general relativity, the causal structure between events is dynamical, but it is definite and observer-independent; events are point-like and the membership of an event A in the future or past light-cone of an event B is an…

Quantum Physics · Physics 2020-07-13 Philippe Allard Guérin , Časlav Brukner

Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-08-12 Seyed Hossein Haeri , Peter Van Roy , Carlos Baquero , Christopher Meiklejohn

We reformulate Pearl's three rules of do-calculus in the language of completely positive (CP) trace-preserving maps, thereby extending them to quantum systems with entanglement. We prove that Rule~2 fails whenever the underlying process…

Quantum Physics · Physics 2025-08-08 Jordi Vallverdu

Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One…

Machine Learning · Statistics 2011-12-01 Pedro A. Ortega