Quantum Natural Language Processing

Fuente: arXiv
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Autori principali: Widdows, Dominic, Aboumrad, Willie, Kim, Dohun, Ray, Sayonee, Mei, Jonathan
Natura: Preprint
Pubblicazione: 2024
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author Widdows, Dominic
Aboumrad, Willie
Kim, Dohun
Ray, Sayonee
Mei, Jonathan
author_facet Widdows, Dominic
Aboumrad, Willie
Kim, Dohun
Ray, Sayonee
Mei, Jonathan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_19758
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantum Natural Language Processing
Widdows, Dominic
Aboumrad, Willie
Kim, Dohun
Ray, Sayonee
Mei, Jonathan
Quantum Physics
Artificial Intelligence
Computation and Language
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.
title Quantum Natural Language Processing
topic Quantum Physics
Artificial Intelligence
Computation and Language
url https://arxiv.org/abs/2403.19758