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Found 7 papers, 3 papers with code
A Pipeline For Discourse Circuits From CCG
DisCoCirc represents natural language text as a `circuit' that captures the core semantic information of the text.
Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning
Here we elevate ZX to an analytical perspective by realising differentiation and integration entirely within the framework of ZX-calculus.
A Quantum Natural Language Processing Approach to Musical Intelligence
In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars.
Representing and Implementing Matrices Using Algebraic ZX-calculus
In linear algebra applications, elementary matrices hold a significant role.
lambeq: An Efficient High-Level Python Library for Quantum NLP
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
A CCG-Based Version of the DisCoCat Framework
While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars.
Diagrammatic Differentiation for Quantum Machine Learning
We introduce diagrammatic differentiation for tensor calculus by generalising the dual number construction from rigs to monoidal categories.
