Quantum Operations for Natural Language Processing

自然语言处理（NLP）是人工智能的一个分支，旨在帮助计算机理解人类语言。NLP的应用在日常生活中很常见，例如在谷歌等搜索引擎和Alexa和Siri等聊天机器人中。尽管这些应用取得了成功，但NLP仍然面临着局限性。这些包括大量的输入数据，因此与某些任务相关的计算时间。此外，目前NLP中使用的自然语言模型并不完美：计算机的输出通常与人类的反应非常不同，正如人们经常在聊天机器人的反应和谷歌的句子翻译中看到的那样。量子自然语言处理（QNLP）与量子机器学习有点相关，是一个最近的研究领域，旨在利用量子计算机在NLP应用中的优势。事实上，量子计算机在解决某些问题（包括优化问题）时，表现优于最著名的经典算法。此外，QNLP的动机不仅仅是计算上的考虑。事实上，一些类似量子的认知和自然语言模型已经浮出水面，这代表了量子人工智能的一个相当大的论点。在这个项目中，我们将专注于自然语言的方面，这些方面与量子理论的概念有直接的相似之处，例如歧义和上下文。然后，这些可以用来提出量子计算机在NLP中的实际应用。该项目将主要是理论性的;然而，如果理论结果允许，我们也将致力于在小规模量子计算机上实现QNLP算法。

Natural Language Processing (NLP) is a branch of Artificial Intelligence that aims to help computers understand human languages. Applications of NLP are commonly found in everyday life, e.g. in search engines such as Google and chatbots such as Alexa and Siri. Despite the success of these applications, NLP still faces limitations. These include the large amount of input data, and hence the computational time associated with certain tasks. Furthermore, current models of natural language used in NLP are not perfect: computers' output is usually very distinct from human responses, as one often witnesses in chatbot responses and Google's translations of sentences. Somewhat related to Quantum Machine Learning, Quantum Natural Language Processing (QNLP) is a recent field of research that aims at exploiting the advantages of quantum computers for NLP applications. Indeed, quantum computers are known to be able to outperform the best-known classical algorithms solving certain problems, including optimization problems. In addition, the motivation for QNLP goes further than the mere computational considerations. Indeed, some quantum-like models of cognition and natural language have surfaced, which represent a considerable argument for quantum artificial intelligence.In this project, we will focus on aspects of natural language which have a direct analogue with concepts of quantum theory, e.g. ambiguity and contextuality. These could then be used to come up with practical use of quantum computers for NLP. The project will be mainly theoretical; however, if theoretical results allow it, we will also aim at implementing QNLP algorithms on small- scale quantum computers.