Quantum Transfer Learning for Acceptability Judgements
Giuseppe Buonaiuto Raffaele Guarasci Aniello Minutolo Giuseppe De Pietro Massimo Esposito
Hybrid quantum-classical classifiers promise to positively impact critical
aspects of natural language processing tasks, particularly
classification-related ones. Among the possibilities currently investigated,
quantum transfer learning, i.e., using a quantum circuit for fine-tuning
pre-trained classical models for a specific task, is attracting significant
attention as a potential platform for proving quantum advantage.
  This work shows potential advantages, both in terms of performance and
expressiveness, of quantum transfer learning algorithms trained on embedding
vectors extracted from a large language model to perform classification on a
classical Linguistics task: acceptability judgments. Acceptability judgment is
the ability to determine whether a sentence is considered natural and
well-formed by a native speaker. The approach has been tested on sentences
extracted from ItaCoLa, a corpus that collects Italian sentences labeled with
their acceptability judgment. The evaluation phase shows results for the
quantum transfer learning pipeline comparable to state-of-the-art classical
transfer learning algorithms, proving current quantum computers' capabilities
to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative
linguistic analysis, aided by explainable AI methods, reveals the capabilities
of quantum transfer learning algorithms to correctly classify complex and more
structured sentences, compared to their classical counterpart. This finding
sets the ground for a quantifiable quantum advantage in NLP in the near future.