PIRSA:23080034

Video URL

https://pirsa.org/23080034

From quantum picturalism to quantum NLP and quantum AI

Coecke, B. (2023). From quantum picturalism to quantum NLP and quantum AI. Perimeter Institute for Theoretical Physics. https://pirsa.org/23080034

Coecke, Bob. From quantum picturalism to quantum NLP and quantum AI. Perimeter Institute for Theoretical Physics, Aug. 25, 2023, https://pirsa.org/23080034 

          @misc{ scivideos_PIRSA:23080034,
            doi = {10.48660/23080034},
            url = {https://pirsa.org/23080034},
            author = {Coecke, Bob},
            keywords = {Quantum Foundations},
            language = {en},
            title = { From quantum picturalism to quantum NLP and quantum AI},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {aug},
            note = {PIRSA:23080034 see, \url{https://scivideos.org/index.php/pirsa/23080034}}
          }

Bob Coecke Quantinuum

Talk numberPIRSA:23080034
DOI10.48660/23080034
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

In 2020 our Oxford-based Quantinuum team performed Quantum Natural Language Processing (QNLP) on IBM quantum hardware [1, 2].  Key to having been able to achieve what is conceived as a heavily data-driven task, is the observation that quantum theory and natural language are governed by much of the same compositional structure -- a.k.a. tensor structure.  

 

Hence our language model is in a sense quantum-native, and we provide an analogy with simulation of quantum systems in terms of algorithmic speed-up [forthcoming].  Meanwhile we have made all our software available open-source, and with support [github.com/CQCL/lambeq].    

 

The compositional match between natural language and quantum extends to other domains than language, and argue that a new generation of AI can emerge when fully pushing this analogy, while exploiting the completeness of categorical quantum mechanics / ZX-calculus [3, 4, 5] for novel reasoning purposes that go hand-in-hand with modern machine learning.

 

[1]     B. Coecke, G. De Felice, K. Meichanetzidis and A. Toumi (2020) Foundations for Near-Term Quantum Natural Language Processing.  https://arxiv.org/abs/2012.03755

[2]     R. Lorenz, A. Pearson, K. Meichanetzidis, D. Kartsaklis and B. Coecke (2020) QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer.  https://arxiv.org/abs/2102.12846

[3]     B. Coecke and A. Kissinger (2017) Picturing Quantum Processes. A first course on quantum theory and diagrammatic reasoning.  Cambridge University Press.

[4]     B. Coecke, D. Horsman, A. Kissinger and Q. Wang (2021) Kindergarten quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus).  https://arxiv.org/abs/2102.10984

[5]     B. Coecke and S. Gogioso (2022) Quantum in Pictures.  Quantinuum, 2023.

Zoom Link: https://pitp.zoom.us/j/92333285960?pwd=MlpJSklmMlVlUlRTTWhsNjc2T2Y4QT09