Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Abstract The Free Energy Principle (FEP) states that under suitable conditions of weak
coupling, random dynamical systems with sufficient degrees of freedom will behave so as to …

Causal modelling provides a powerful set of tools for identifying causal structure from
observed correlations. It is well known that such techniques fail for quantum systems, unless …

Many fields of science investigate states and processes as resources. Chemistry,
thermodynamics, Shannon's theory of communication channels, and the theory of quantum …

We derive quantum theory from purely informational principles. Five elementary axioms—
causality, perfect distinguishability, ideal compression, local distinguishability, and pure …

Reichenbach's principle asserts that if two observed variables are found to be correlated,
then there should be a causal explanation of these correlations. Furthermore, if the …

We provide conceptual and mathematical foundations for near-term quantum natural
language processing (QNLP), and do so in quantum computer scientist friendly terms. We …

We propose a mathematical framework for a unification of the distributional theory of
meaning in terms of vector space models, and a compositional theory for grammatical types …

This paper has two tightly intertwined aims:(i) to introduce an intuitive and universal
graphical calculus for multi-qubit systems, the ZX-calculus, which greatly simplifies …

Our common understanding of the physical world deeply relies on the notion that events are
ordered with respect to some time parameter, with past events serving as causes for future …