Explaining quantum correlations through evolution of causal models
نویسندگان
چکیده
منابع مشابه
On Causal Explanations of Quantum Correlations
The framework of causal models is ideally suited to formalizing certain conceptual problems in quantum theory, and conversely, a variety of tools developed by physicists studying the foundations of quantum theory have applications for causal inference. This talk reviews some of the connections between the two fields. In particular, it is shown that certain correlations predicted by quantum theo...
متن کاملQuantum correlations with no causal order
The idea that events obey a definite causal order is deeply rooted in our understanding of the world and at the basis of the very notion of time. But where does causal order come from, and is it a necessary property of nature? Here, we address these questions from the standpoint of quantum mechanics in a new framework for multipartite correlations that does not assume a pre-defined global causa...
متن کاملEvolution in Quantum Causal Histories
We provide a precise definition and analysis of quantum causal histories (QCH’s). A QCH consists of a discrete, locally finite, causal pre-spacetime with matrix algebras encoding the quantum structure at each event. The evolution of quantum states and observables is described by completely positive maps between the algebras at causally related events. We show that this local description of evol...
متن کاملQuantum Common Causes and Quantum Causal Models
John-Mark A. Allen, Jonathan Barrett, Dominic C. Horsman, Ciarán M. Lee, and Robert W. Spekkens Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom Department of Physics, University of Durham, South Road, Durham DH1 3LE, United Kingdom Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, Uni...
متن کاملQuantum Models of Human Causal Reasoning 1 Running head: QUANTUM MODELS OF HUMAN CAUSAL REASONING Quantum Models of Human Causal Reasoning
Throughout our lives, we are constantly faced with a variety of causal reasoning problems. A challenge for cognitive modelers is developing a comprehensive framework for modeling causal reasoning across different types of tasks and levels of causal complexity. Causal graphical models (CGMs), based on Bayes’ calculus, have perhaps been the most successful at explaining and predicting judgments o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review A
سال: 2017
ISSN: 2469-9926,2469-9934
DOI: 10.1103/physreva.95.042120