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Related papers: Non-causal computation

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It is usually assumed that a quantum computation is performed by applying gates in a specific order. One can relax this assumption by allowing a control quantum system to switch the order in which the gates are applied. This provides a more…

Quantum Physics · Physics 2020-06-11 Mateus Araújo , Fabio Costa , Časlav Brukner

Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron…

Neurons and Cognition · Quantitative Biology 2023-10-17 Rajkumar Vasudeva Raju , Zhe Li , Scott Linderman , Xaq Pitkow

It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, due to quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal…

Quantum Physics · Physics 2018-03-28 Esteban Castro-Ruiz , Flaminia Giacomini , Časlav Brukner

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical…

Quantum Physics · Physics 2023-03-27 Dominic Widdows , Jyoti Rani , Emmanuel Pothos

In decentralized stochastic control (or stochastic team theory) and game theory, if there is a pre-defined order in a system in which agents act, the system is called \textit{sequential}, otherwise it is non-sequential. Much of the…

Optimization and Control · Mathematics 2025-08-05 Omar Mrani-Zentar , Ryan Simpson , Serdar Yüksel

Causality among events is widely recognized as a most fundamental structure of spacetime, and causal sets have been proposed as discrete models of the latter in the context of quantum gravity theories, notably in the Causal Set Programme.…

Computational Physics · Physics 2010-04-20 Tommaso Bolognesi

Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain challenging, as most existing algorithms assume that the…

Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of…

Artificial Intelligence · Computer Science 2013-03-08 John F. Lemmer

This paper presents a theory of systemic undecidability, reframing incomputability as a structural property of systems rather than a localized feature of specific functions or problems. We define a notion of causal embedding and prove a…

Logic in Computer Science · Computer Science 2025-09-03 Seth Bulin

Causal modelling frameworks link observable correlations to causal explanations, which is a crucial aspect of science. These models represent causal relationships through directed graphs, with vertices and edges denoting systems and…

Quantum Physics · Physics 2025-02-10 Carla Ferradini , Victor Gitton , V. Vilasini

In recent years, various frameworks have been proposed for the study of quantum processes with indefinite causal order. In particular, quantum circuits with quantum control of causal order (QC-QCs) form a broad class of physical supermaps…

Quantum Physics · Physics 2025-07-14 Maarten Grothus , Alastair A. Abbott , Augustin Vanrietvelde , Cyril Branciard

Consider a graph having quantum systems lying at each node. Suppose that the whole thing evolves in discrete time steps, according to a global, unitary causal operator. By causal we mean that information can only propagate at a bounded…

Discrete Mathematics · Computer Science 2021-11-04 Pablo Arrighi , Simon Martiel

Quantum computation with quantum data that can traverse closed timelike curves represents a new physical model of computation. We argue that a model of quantum computation in the presence of closed timelike curves can be formulated which…

Quantum Physics · Physics 2008-11-26 Dave Bacon

Existing abstract models of quantum computation make reference to circuit elements, much in contrast to their classical counterparts. Circuits, as a model of computation, substantially limit algorithmic expression and obscure high-level…

Quantum Physics · Physics 2023-07-18 Santiago Núñez-Corrales

The quantum switch, the canonical example of a process with indefinite causal order, has been claimed to provide various advantages over processes with definite causal orders for some particular tasks in the field of quantum metrology. In…

Quantum Physics · Physics 2024-06-27 Raphaël Mothe , Cyril Branciard , Alastair A. Abbott

Leveraging topos theory a semantics can be given to sequential circuits where time-sensitive gates, such as unit delay, are treated uniformly with combinational gates. Both kinds of gates are functions in a particular topos: the topos of…

Logic in Computer Science · Computer Science 2018-07-20 Arnaud Spiwack

It is well-known that if one assumes quantum theory to hold locally, then processes with indefinite causal order and cyclic causal structures become feasible. Here, we study qualitative limitations on causal structures and correlations…

Quantum Physics · Physics 2024-01-09 Eleftherios-Ermis Tselentis , Ämin Baumeler

The standard model of quantum circuits assumes operations are applied in a fixed sequential "causal" order. In recent years, the possibility of relaxing this constraint to obtain causally indefinite computations has received significant…

Quantum Physics · Physics 2024-08-20 Alastair A. Abbott , Mehdi Mhalla , Pierre Pocreau

When considering a sequent-style proof system for quantum programs, there are certain elements of quantum mechanics that we may wish to capture, such as phase, dynamics of unitary transformations, and measurement probabilities. Traditional…

Logic in Computer Science · Computer Science 2016-06-23 Cameron Beebe

Real-world problems, for example in climate applications, often require causal reasoning on spatially gridded time series data or data with comparable structure. While the underlying system is often believed to behave similarly at different…

Machine Learning · Computer Science 2026-02-16 Martin Rabel , Jakob Runge