In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars.
Inspired by work on compositionality in quantum theory, and categorical quantum mechanics in particular, we propose the notions of Schrodinger, Whitehead, and complete compositionality.
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
We also how how linguistic model of space can interact with other such models related to our senses and/or embodiment, such as the conceptual spaces of colour, taste and smell, resulting in a rich compositional model of meaning that is close to human experience and embodiment in the world.
We cast aspects of consciousness in axiomatic mathematical terms, using the graphical calculus of general process theories (a. k. a symmetric monoidal categories and Frobenius algebras therein).
Diagrammatically speaking, grammatical calculi such as pregroups provide wires between words in order to elucidate their interactions, and this enables one to verify grammatical correctness of phrases and sentences.
Because QA4ML users have to view a non-trivial amount of data and perform many input actions to correct errors made by the ML model, an optimally-designed user interface (UI) can reduce the cost of interactions significantly.
Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware.
This paper is a `spiritual child' of the 2005 lecture notes Kindergarten Quantum Mechanics, which showed how a simple, pictorial extension of Dirac notation allowed several quantum features to be easily expressed and derived, using language even a kindergartner can understand.
Natural language processing (NLP) is at the forefront of great advances in contemporary AI, and it is arguably one of the most challenging areas of the field.
We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar.
In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP.
Categorical compositional distributional semantics provide a method to derive the meaning of a sentence from the meaning of its individual words: the grammatical reduction of a sentence automatically induces a linear map for composing the word vectors obtained from distributional semantics.
Categorical compositional distributional semantics (CCDS) allows one to compute the meaning of phrases and sentences from the meaning of their constituent words.
Moreover, the interaction between the three disciplines of cognitive science, linguistics and game theory is a fertile ground for research.
We accommodate the Integrated Connectionist/Symbolic Architecture (ICS) of  within the categorical compositional semantics (CatCo) of , forming a model of categorical compositional cognition (CatCog).
We address the value of quantum RAM (Giovannetti, 2008) for this model and extend an algorithm from Wiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences in CSC.
In this paper we argue for a paradigmatic shift from `reductionism' to `togetherness'.
Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics.
Moreover, just like CQM allows for varying the model in which we interpret quantum axioms, one can also vary the model in which we interpret word meaning.
The first QPL under the new name Quantum Physics and Logic was held in Reykjavik (2008), followed by Oxford (2009 and 2010), Nijmegen (2011), Brussels (2012) and Barcelona (2013).
Within the categorical compositional distributional model of meaning, we provide semantic interpretations for the subject and object roles of the possessive relative pronoun `whose'.
This paper develops a compositional vector-based semantics of subject and object relative pronouns within a categorical framework.
They also provide semantics for Lambek's pregroup algebras, applied to formalizing the grammatical structure of natural language, and are implicit in a distributional model of word meaning based on vector spaces.
We survey some basic mathematical structures, which arguably are more primitive than the structures taught at school.
We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation.
We put forward a new take on the logic of quantum mechanics, following Schroedinger's point of view that it is composition which makes quantum theory what it is, rather than its particular propositional structure due to the existence of superpositions, as proposed by Birkhoff and von Neumann.
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, for which we rely on the algebra of Pregroups, introduced by Lambek.