量子自然语言处理
Quantum Natural Language Processing
AI总结：
本文综述量子自然语言处理领域现状，考察词嵌入、序列模型等技术，提出文本编码的新量子设计，并指出现代AI的“幻觉”源于对事实概念化的误解。
摘要：
语言处理是当前人工智能发展的核心，与此同时量子计算机正逐步投入使用，这引发了人们对量子自然语言处理的浓厚兴趣，并催生了若干早期提案与实验。\n本文综述了该领域的研究现状，展示了自然语言处理相关技术在量子语言处理中的应用方式。我们考察了词嵌入与序列模型的现有技术水平，提出了若干未来研究方向，并探讨了这些方向存在的权衡取舍。我们还重点介绍了近期在Transformer模型中计算注意力以及执行语法解析的一些方法。此外，我们针对文本编码这一基础任务（即在内存中表示字符串）提出了一种新的量子设计，该任务此前尚未得到详细研究。\n量子理论为量化不确定性以及解释“何为智能？”做出了贡献。在此背景下，我们认为，现代人工智能系统中的“幻觉”是对事实概念化方式的一种误解：语言可以表达许多看似合理的假设，其中只有少数会成为现实。
英文摘要：
Language processing is at the heart of current developments in artificial intelligence, and quantum computers are becoming available at the same time. This has led to great interest in quantum natural language processing, and several early proposals and experiments. This paper surveys the state of this area, showing how NLP-related techniques have been used in quantum language processing. We examine the art of word embeddings and sequential models, proposing some avenues for future investigation and discussing the tradeoffs present in these directions. We also highlight some recent methods to compute attention in transformer models, and perform grammatical parsing. We also introduce a new quantum design for the basic task of text encoding (representing a string of characters in memory), which has not been addressed in detail before. Quantum theory has contributed toward quantifying uncertainty and explaining "What is intelligence?" In this context, we argue that "hallucinations" in modern artificial intelligence systems are a misunderstanding of the way facts are conceptualized: language can express many plausible hypotheses, of which only a few become actual.
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。
