首个可生成连贯对话的量子-经典混合语言模型
Hybrid Quantum Transformer for Language Generation
- 将变分量子电路嵌入Transformer架构，实现量子与经典计算协同
- 仅用10个量子比特和80个门即可替代150M模型中约10%的参数
- 为大规模语言生成引入量子计算提供可行性验证，适合量子AI研究者
尽管量子计算逐渐被用于替代经典计算，但现有量子或混合模型仍局限于简单任务，尚未在大规模自然语言生成中取得成功。本文提出首个用于自然语言生成的混合量子-经典大语言模型HyQuT，能够实现连贯且上下文感知的对话。该架构在800万和1.5亿参数规模下，将变分量子电路（VQCs）集成至Transformer框架中。实验表明，仅需10个量子比特和80个量子门，即可替代1.5亿参数模型中约10%的经典参数，同时保持相当的收敛稳定性与生成质量。本研究首次展示了将量子计算融入大规模生成式语言模型的可行性。
原文摘要 · Abstract (English)
Although quantum computing has been increasingly applied to replace classical computation, most existing quantum or hybrid models remain confined to simple tasks, with no successful application to large-scale natural language generation to date. In this work, we present the first hybrid quantum-classical large language model (LLM) for natural language generation, HyQuT, capable of performing coherent and context-aware dialogue. The proposed architecture integrates variational quantum circuits (VQCs) into the Transformer framework at both 8M and 150M parameter scales. Experimental results show that a minimal number of qubits (10 qubits with 80 quantum gates) can replace about 10% of the classical parameters in the 150M-parameter model, while achieving comparable convergence stability and generation quality. This study provides an early demonstration of the feasibility of integrating quantum computing to large-scale generative language models.
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