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CATEGORICAL LOGIC

## An Introduction to Effectus Theory

Resource type

Authors/contributors

- Cho, Kenta (Author)
- Jacobs, Bart (Author)
- Westerbaan, Bas (Author)
- Westerbaan, Abraham (Author)

Title

An Introduction to Effectus Theory

Abstract

Effectus theory is a new branch of categorical logic that aims to capture the essentials of quantum logic, with probabilistic and Boolean logic as special cases. Predicates in effectus theory are not subobjects having a Heyting algebra structure, like in topos theory, but `characteristic' functions, forming effect algebras. Such effect algebras are algebraic models of quantitative logic, in which double negation holds. Effects in quantum theory and fuzzy predicates in probability theory form examples of effect algebras. This text is an account of the basics of effectus theory. It includes the fundamental duality between states and effects, with the associated Born rule for validity of an effect (predicate) in a particular state. A basic result says that effectuses can be described equivalently in both `total' and `partial' form. So-called `commutative' and `Boolean' effectuses are distinguished, for probabilistic and classical models. It is shown how these Boolean effectuses are essentially extensive categories. A large part of the theory is devoted to the logical notions of comprehension and quotient, which are described abstractly as right adjoint to truth, and as left adjoint to falisity, respectively. It is illustrated how comprehension and quotients are closely related to measurement. The paper closes with a section on `non-commutative' effectus theory, where the appropriate formalisation is not entirely clear yet.

Publication

arXiv:1512.05813 [quant-ph]

Date

2015-12-17

Accessed

2019-11-23T16:39:44Z

Library Catalog

Extra

ZSCC: 0000039 arXiv: 1512.05813

Citation

Cho, K., Jacobs, B., Westerbaan, B., & Westerbaan, A. (2015). An Introduction to Effectus Theory.

*ArXiv:1512.05813 [Quant-Ph]*. Retrieved from http://arxiv.org/abs/1512.05813
CATEGORICAL LOGIC

PROBABILITY & STATISTICS

PROGRAMMING LANGUAGES

Methodology

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