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Related papers: Quantum causal models via QBism

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Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. In this paper, we put forth a theoretical framework for merging quantum information science and causal…

Quantum Physics · Physics 2022-12-21 Mohammad Ali Javidian , Vaneet Aggarwal , Zubin Jacob

We propose an exercise in which one attempts to deduce the formalism of quantum mechanics solely from phenomenological observations. The only assumed inputs are obtained through sequential probing of quantum systems; no presuppositions…

Contextuality is a defining feature that separates the quantum from the classical descriptions of physical systems. Within the marginal-scenario framework, noncontextual models are characterized by the existence of a single joint…

Quantum Physics · Physics 2026-04-08 Andrea Navoni , Marco G. Genoni , Andrea Smirne

Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able…

Artificial Intelligence · Computer Science 2015-05-19 David Heckerman , Ross D. Shachter

In the Bayesian approach to probability theory, probability quantifies a degree of belief for a single trial, without any a priori connection to limiting frequencies. In this paper we show that, despite being prescribed by a fundamental…

Quantum Physics · Physics 2009-11-07 Carlton M. Caves , Christopher A. Fuchs , Ruediger Schack

We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to…

Artificial Intelligence · Computer Science 2013-03-08 Marek J. Druzdzel , Herbert A. Simon

Event centric approaches to modeling physics have gained traction in recent decades. In this work, we present a first principles approach to this idea, which assumes nothing but the existence of causal networks of events and their…

General Physics · Physics 2023-06-28 Sam Powers , Dejan Stojkovic

Quantum mechanics may be formulated as SENSIBLE QUANTUM MECHANICS (SQM) so that it contains nothing probabilistic, except, in a certain frequency sense, conscious perceptions. Sets of these perceptions can be deterministically realized with…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Don N. Page

The status of locality in quantum mechanics is analyzed from a nonstandard point of view. It is assumed that quantum states are relative, they depend on and are defined with respect to some bigger physical system which contains the former…

Quantum Physics · Physics 2007-05-23 Gyula Bene

Explaining observations in terms of causes and effects is central to all of empirical science. Correlations between entangled quantum particles, however, seem to defy such an explanation. To recover a causal picture in this case, some of…

Quantum Physics · Physics 2016-09-05 M. Ringbauer , C. Giarmatzi , R. Chaves , F. Costa , A. G. White , A. Fedrizzi

While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict, for example, how cognitive functions are affected by external interventions that perturb neuronal…

Neurons and Cognition · Quantitative Biology 2021-04-12 Sebastian Weichwald , Jonas Peters

Quantum mechanics permits nonlocality - both nonlocal correlations and nonlocal equations of motion - while respecting relativistic causality. Is quantum mechanics the unique theory that reconciles nonlocality and causality? We consider two…

Quantum Physics · Physics 2008-02-03 Sandu Popescu , Daniel Rohrlich

The goal of causal inference is to understand the outcome of alternative courses of action. However, all causal inference requires assumptions. Such assumptions can be more influential than in typical tasks for probabilistic modeling, and…

Methodology · Statistics 2016-10-31 Dustin Tran , Francisco J. R. Ruiz , Susan Athey , David M. Blei

There are two major approaches to building good machine learning algorithms: feeding lots of data into large models, or picking a model class with an ''inductive bias'' that suits the structure of the data. When taking the second approach…

Quantum Physics · Physics 2023-12-08 Kaitlin Gili , Guillermo Alonso , Maria Schuld

There has been a body of works deriving the complex Hilbert space structure of quantum theory from axioms/principles/postulates to deepen our understanding about quantum theory and to reveal ways to go beyond it to resolve foundational…

Quantum Physics · Physics 2018-09-26 Ding Jia

Bell non-local correlations cannot be naturally explained in a fixed causal structure. This serves as a motivation for considering models where no global assumption is made beyond logical consistency. The assumption of a fixed causal order…

Quantum Physics · Physics 2016-04-06 Ämin Baumeler , Stefan Wolf

In general relativity, `causal structure' refers to the partial order on space-time points (or regions) that encodes time-like relationships. Recently, quantum information and quantum foundations saw the emergence of a `causality…

Quantum Physics · Physics 2017-08-16 Aleks Kissinger , Matty Hoban , Bob Coecke

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

Pivotal within quantum physics, the concept of quantum incompatibility is generally related to algebraic aspects of the formalism, such as commutation relations and unbiasedness of bases. Recently, the concept was identified as a resource…

Quantum Physics · Physics 2020-12-24 E. Martins , M. F. Savi , R. M. Angelo

The standard approach to causal modelling especially in social and health sciences is the potential outcomes framework due to Neyman and Rubin. In this framework, observations are thought to be drawn from a distribution over variables of…

Methodology · Statistics 2025-07-18 Benedikt Höltgen , Robert C. Williamson