Foundations for Near-Term Quantum Natural Language Processing

TL;DR

This paper establishes foundational concepts for near-term quantum natural language processing (QNLP), highlighting its quantum-native nature, advantages of NISQ hardware, and the use of diagrammatic reasoning for encoding linguistic structure.

Contribution

It introduces a comprehensive framework for QNLP that leverages quantum models, diagrammatic reasoning, and NISQ-friendly encoding, advancing the integration of linguistic structure with quantum computing.

Findings

Quantum speed-up applies to a broader range of QNLP tasks.

NISQ hardware enables efficient encoding of linguistic structure.

Diagrammatic formalism facilitates translation of language models into quantum circuits.

Abstract

We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.