Text Summarisation with Quantum Natural Language Processing (Internship)
Quantum natural language processing (QNLP) is the use of quantum computing to solve NLP tasks faster than any classical computer.
In a recent approach1, text is represented as parameterised circuits that are optimised using a hybrid classical-quantum algorithm. This approach was implemented on noisy intermediate-scale quantum (NISQ) hardware, with promising experimental results on text classification and question answering.
The aim of the internship is to apply QNLP to the problem of automatic text summarisation.2 The student will design quantum algorithms, investigate their asymptotic speedup compared to classical ones and implement proof-of-concept experiments to evaluate them.
The internship will be hosted at the Laboratoire d’Informatique & Systèmes in Marseille, supervised by:
- Alexis Toumi (email@example.com)
- Benoit Favre (firstname.lastname@example.org)
- Giuseppe di Molfetta (email@example.com)
Send an email to the people above with:
- a CV
- a cover letter
- an academic transcript
Bob Coecke, Giovanni de Felice, Konstantinos Meichanetzidis and Alexis Toumi Foundations for near-term quantum natural language processing (2020) ↩
Dan Gillick and Benoit Favre A scalable global model for summarization (2009) ↩