Bridging Classical and Quantum Computing for Next-Generation Language Models

TL;DR

This paper presents AQCF, a dynamic framework that integrates classical and quantum computing for language models, overcoming NISQ device limitations through real-time adaptation and innovative quantum-classical co-design.

Contribution

It introduces the first adaptive quantum-classical fusion framework that dynamically manages processing, enhancing quantum resource efficiency and compatibility with classical Transformers for language tasks.

Findings

Achieves competitive sentiment analysis performance

Significantly improves quantum resource efficiency

Operates successfully within NISQ device constraints

Abstract

Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, including barren plateaus and limited coherence. Current approaches often fail due to static quantum-classical partitioning. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge this gap through dynamic, quantum-classical co-design. AQCF's core principle is real-time adaptation: it analyzes input complexity to orchestrate seamless transitions between classical and quantum processing. The framework features three key innovations: (1) entropy-driven adaptive circuits that circumvent barren plateaus; (2) quantum memory banks that unify classical attention with quantum state-based similarity retrieval; and (3) intelligent fusion controllers that allocate tasks for optimal performance. This architecture maintains full compatibility with classical Transformers while progressively incorporating quantum advantages. Experiments on sentiment analysis demonstrate that AQCF achieves competitive performance, significantly improves quantum resource efficiency, and operates successfully within typical NISQ constraints. By providing a seamless integration pathway, AQCF offers both immediate practical value on current quantum hardware and a clear evolution path toward mature Quantum LLMs.