  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.
