量子计算机上的语法感知句子分类
Grammar-aware sentence classification on quantum computers
摘要：
自然语言处理（NLP）处于当代人工智能巨大进步的最前沿，可以说是该领域最具挑战性的领域之一。与此同时，在量子计算（QC）领域，随着量子硬件的稳步增长和量子算法实现的显著改进，我们正在接近一个时代，量子计算机可以执行传统计算机无法完成的任务。这为人工智能，特别是NLP提供了一系列新的机会。在这项工作中，我们使用自然语言意义的分类分布组成（DiscoCat）模型，其基础数学基础使其适合量子实例化。容错量子算法的早期工作已经证明了NLP的潜在量子优势，特别是使用DisCoCat。在这项工作中，我们专注于有噪声的中间尺度量子（NISQ）硬件的能力，并使用DisCoCat框架在NISQ处理器上执行NLP任务的第一个实现。句子被实例化为参数化的量子线路;使用参数化的量子电路将词义嵌入量子态中，并且句子的语法结构忠实地表现为将词电路组成一个量子电路的纠缠操作模式。在二进制分类的监督NLP任务中使用经典优化器训练电路的参数。我们的新型QNLP模型显示出了可扩展性的具体前景，因为量子硬件的质量在不久的将来会得到提高，并在QC和AI的交叉点上巩固了实验研究的一个新分支。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
