Quantum Natural Language Processing
arXiv:2403.19758 · doi:10.1007/s13218-024-00861-w
Abstract
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.
References in corpus (19)
- Quantum algorithm for solving linear systems of equations
- Quantum random access memory
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- High-threshold and low-overhead fault-tolerant quantum memory
- Experimental Quantum Generative Adversarial Networks for Image Generation
- Quantum Neuron: an elementary building block for machine learning on quantum computers
- QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
- Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
- PEER: A Collaborative Language Model
- Quantum pixel representations and compression for -dimensional images
- Near-Term Advances in Quantum Natural Language Processing
- Introducing Non-Linear Activations into Quantum Generative Models
- Quantum Financial Modeling on Noisy Intermediate-Scale Quantum Hardware: Random Walks using Approximate Quantum Counting
- Quantum Text Encoding for Classification Tasks
- Synthesis of Quantum Vector Databases Based on Grovers Algorithm
- Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
- Peptide Binding Classification on Quantum Computers
- Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
- Quantum Natural Language Generation on Near-Term Devices