Bridging Classical and Quantum Computing for Next-Generation Language Models

Abstract

The remarkable success of Transformer architectures in Large Language Models (LLMs) has revolutionized natural language processing, yet the transition to quantum computing for next-generation language models remains an open challenge. While quantum computing promises exponential advantages, a fundamental gap exists between classical deep learning and quantum computing paradigms, particularly given the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, including barren plateaus, limited qubit coherence, and circuit depth restrictions. We present Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge classical and quantum computing for language models by reimagining Transformer architectures through quantum-classical co-design. Our key insight is that effective bridging requires dynamic adaptation rather than static translation—the framework analyzes input complexity in real-time to orchestrate seamless transitions between classical and quantum processing. AQCF introduces entropy-driven adaptive circuits that circumvent barren plateaus, quantum memory banks that unify classical attention with quantum state-based similarity retrieval, and intelligent fusion controllers that ensure each computational paradigm handles tasks where it naturally excels. This bridging architecture maintains full compatibility with existing classical Transformers while progressively incorporating quantum advantages as they become accessible. Experiments on sentiment analysis demonstrate that AQCF achieves competitive performance while significantly improving quantum resource efficiency, operating successfully within typical NISQ constraints. By establishing a seamless integration pathway from today's classical LLMs to tomorrow's quantum-enhanced models, our framework provides both immediate practical value on current quantum hardware and a clear evolution path toward full Quantum LLMs as technology matures.