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Using Quantum Computing in Natural Language Processing

Informationen zur Arbeit

Abschlussarbeitstyp: Master, Diplom
Betreuer: Michael Färber
Forschungsgruppe: Web Science

Archivierungsnummer: 4864
Abschlussarbeitsstatus: Offen
Beginn: Fehler: Ungültige Zeitangabe
Abgabe: unbekannt

Weitere Informationen


In recent years, first approaches have been proposed to apply techniques of quantum computing [0] to natural language processing (NLP) tasks, such as machine translation, question answering, and relation extraction from text. However, the practical applicability of quantum NLP (QNLP) has been investigated only to a limited degree so far. Examples are given in [1][2].

In this thesis, the student is asked to first review state-of-the-art approaches for se-lected QNLP tasks, such as relation extraction. Based on existing frameworks, such as lambeq, the student will then design, implement, and evaluate experiments – similar to [1] – to see the current limitations and potential of QNLP. The focus will be particularly on scaling up QNLP-implementations as far as possible given available hardware [3].


The student should have solid programming skills in Python. Furthermore, the student should be highly motivated to study the foundations of quantum computing and to proactively work on the project.

[0] [1] [2] [3]

Ausschreibung: Download (pdf)