Natural language processing (NLP) is at the forefront of great advances in contemporary AI, and it is arguably one of the most challenging areas of the field. At the same time, in the area of Quantum Computing (QC), with the steady growth of quantum hardware and notable improvements towards implementations of quantum algorithms, we are approaching an era when quantum computers perform tasks that cannot be done on classical computers with a reasonable amount of resources. This provides a new range of opportunities for AI, and for NLP specifically. In this work, we work with the Categorical Distributional Compositional (DisCoCat) model of natural language meaning, whose underlying mathematical underpinnings make it amenable to quantum instantiations. Earlier work on fault-tolerant quantum algorithms has already demonstrated potential quantum advantage for NLP, notably employing DisCoCat. In this work, we focus on the capabilities of noisy intermediate-scale quantum (NISQ) hardware and perform the first implementation of an NLP task on a NISQ processor, using the DisCoCat framework. Sentences are instantiated as parameterised quantum circuits; word-meanings are embedded in quantum states using parameterised quantum-circuits and the sentence's grammatical structure faithfully manifests as a pattern of entangling operations which compose the word-circuits into a sentence-circuit. The circuits' parameters are trained using a classical optimiser in a supervised NLP task of binary classification. Our novel QNLP model shows concrete promise for scalability as the quality of the quantum hardware improves in the near future and solidifies a novel branch of experimental research at the intersection of QC and AI.

翻译：自然语言处理是当代人工智能前沿进展的核心领域，也是该领域最具挑战性的方向之一。与此同时，在量子计算领域，随着量子硬件的稳步发展及量子算法实现的显著进步，我们正迈向量子计算机能执行经典计算机在合理资源下无法完成的任务的新时代。这为人工智能，特别是自然语言处理，开辟了新的机遇。本研究采用基于范畴分布组合模型的自然语言意义表示模型，其数学基础使其天然适配量子实现。已有关于容错量子算法的研究已展示了量子自然语言处理的潜在优势，尤其是通过该模型实现。本研究聚焦于嘈杂中等规模量子硬件的潜力，首次在量子处理器上实现了基于此框架的自然语言处理任务。句子被实例化为参数化的量子电路，词义通过参数化量子电路嵌入量子态中，句子的语法结构通过纠缠操作模式忠实地显现，将各词电路组合成句子电路。通过经典优化器在二分类监督学习任务中训练电路参数。我们的新型量子自然语言处理模型在量子硬件质量即将提升的背景下展现出显著的可扩展性前景，并巩固了量子计算与人工智能交叉领域的实验研究新分支。