Quantum Natural Language Processing

TL;DR

This paper surveys the nascent field of quantum natural language processing (QNLP), outlining how quantum computing concepts can be harnessed for embedding, sequence modeling, attention, and grammar-based parsing in NLP. It highlights concrete approaches such as the QPOSTR quantum string encoding and quantum embeddings (memory-efficient and circuit-efficient) as well as near-term sequential generation architectures that blend quantum neurons with classical learning. The review discusses both the promise of quantum representations and the substantial practical hurdles, including NISQ-era limitations, barren plateaus in training, and quantum I/O bottlenecks, while also drawing on tensor-network grammars and retrieval-grounded generation to illustrate potential cross-pollination with classical AI. By connecting quantum theory notions of possible versus actual with language generation and fact-checking, the work suggests philosophical and practical avenues for improving reliability and interpretability in AI, even as hardware matures.

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.

Figures (21)

• Figure 1: Single-qubit gates used in this paper, and their corresponding matrices, which operate on the superposition state $\alpha\ket{0} + \beta\ket{1}$ written as the column vector $\alpha\beta^T$.
• Figure 2: Two-qubit CNOT (controlled-$X$) and gate
• Figure 3: Three-qubit multi-controlled gate (Toffoli gate) with $\ket{1}$ and $\ket{0}$ control states.
• Figure 4: Simple encodings for a three-letter alphabet in a two-qubit register, using the convention that the top qubit in the register is the least-significant "units" bit in the binary encoding.
• Figure 5: Position and Character Encoding for the string cab.