Quantum Transfer Learning for Sentiment Analysis: an experiment on an Italian corpus

Exploiting quantum properties to improve performance of different tasks in Natural Language Processing (NLP) and other domains has increasingly becoming a successful trend to deal complex language phenomena or to fill task or domain-specific gaps with an approach that needs less data and minor computational resources. The field that has, to date, yielded more quantum-based attention is the retrieval and classification of textual data. This work aims to replicate the excellent results of hybrid quantum approaches for syntactic tasks on semantic classification tasks. In detail, a quantum machine learning algorithm, namely, the Variational Quantum Classifier (VQC), is used to perform sentiment analysis classification tasks. This algorithm can deduce the relationships between input features and their corresponding class affiliations using a parametrized quantum circuit and an encoding layer that translates classical data into quantum states. The approach has been tested on a well-known benchmark annotated dataset used for the Italian language, and the results have been compared to existing baselines, pointing out state-of-the-art scores.