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QI: Fachverband Quanteninformation
QI 10: Quantum Machine Learning II
QI 10.1: Vortrag
Dienstag, 11. März 2025, 11:00–11:15, HS VIII
Quantum Machine Learning for Natural Language Processing — •Charles Varmantchaonala M.1, Jean Louis E. K. Fendji2,3, and Christopher Gies1 — 1Institut für Physik, Fakultät V, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg — 2Department of Computer Engineering, University Institute of Technology, University of Ngaoundere, P.O. Box 454 Ngaoundere, Cameroon — 3Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
Quantum Machine Learning (QML) offers exciting possibilities for improving many fields by leveraging the unique properties of quantum mechanics to solve problems more efficiently. Natural Language Processing (NLP) is a key area of artificial intelligence that focuses on helping machines understand and work with human language. The intersection of NLP and QML -- Quantum Natural Language Processing (QNLP) -- is a new and intriguing research field [1], as it could lead to major improvements in how machines understand human languages, process meaning, and handle complex linguistic tasks. Exploring how QML and NLP can work together is important, as it may provide better solutions and more accurate models for language understanding. This talk will explore the current progress in both QML and QNLP, and explore the aspect of classical-to-quantum sentence or sequence encoding.
[1] Varmantchaonala, C. M., Fendji, J. L. E., Schöning, J., & Atemkeng, M. (2024). Quantum Natural Language Processing: A Comprehensive Survey. IEEE Access.
Keywords: Quantum Machine Learning; Quantum Natural Language Processing; Quantum Word Embedding; Natural Language Processing; Quantum Computing