论文标题
量子自然语言处理的温和介绍
A gentle introduction to Quantum Natural Language Processing
论文作者
论文摘要
本主题论文的主要目标是以NLP工程师和量子计算从业者可以理解的方式引入量子自然语言处理（QNLP）。 QNLP是量子计算的最新应用，旨在将句子的含义表示为编码量子计算机的向量。为了实现这一目标，单词的分布含义是通过句子的组成含义扩展的：代表单词含义的向量是通过句子的句法结构组成的。这是使用基于张量产品的算法完成的。我们看到，该算法在古典计算机上效率低下，但使用量子电路很好地缩放。揭露其实施的实际细节后，我们将进行三个用例。
The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.
