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.

翻译：混合量子-经典分类器有望对自然语言处理任务中的关键方面产生积极影响，尤其是与分类相关的任务。在当前研究的诸多可能性中，量子迁移学习（即使用量子电路对预训练经典模型进行特定任务的微调）作为证明量子优势的潜在平台正吸引广泛关注。本工作展示了量子迁移学习算法在性能和表达能力方面的潜在优势，该算法基于从大语言模型中提取的嵌入向量进行训练，以执行经典语言学任务：可接受性判断。可接受性判断是指判断一个句子是否被母语者视为自然且结构良好的能力。该方法已在从ItaCoLa语料库（收录了带有可接受性标注的意大利语句子的语料库）中提取的句子上进行了测试。评估阶段显示，量子迁移学习管道的性能与经典迁移学习算法的最新技术水平相当，证明了当前量子计算机处理自然语言任务并应用于实际场景的能力。此外，借助可解释人工智能方法进行的定性语言学分析表明，与经典算法相比，量子迁移学习算法能更准确地分类复杂且结构更严密的句子。这一发现为在不久的将来自然语言处理领域实现可量化的量子优势奠定了基础。