用量子启发方法提升大模型微调效率，降低资源消耗。
How Can Quantum Deep Learning Improve Large Language Models?
- 引入量子幅值嵌入框架，以少量参数实现高效模型更新。
- 相比传统方法，该框架在收敛速度与表示能力上表现更优。
- 适合关注高效微调与未来量子计算融合的研究者。
大型语言模型（LLMs）的快速发展推动了自然语言处理的进步，但高效适应仍面临挑战。全量微调虽性能优异，却带来高昂的计算与内存开销。参数高效微调（PEFT）方法如低秩适应（LoRA）、前缀微调和稀疏低秩适应（SoRA）通过减少可训练参数来缓解此问题，同时保持竞争力的准确率。然而，这些方法在可扩展性、稳定性和跨任务泛化方面仍存在局限。量子深度学习的进展为解决这些问题提供了新思路，特别是通过量子启发编码和参数化量子电路（PQCs）。其中，量子幅值嵌入适应（QAA）框架展示了以极小开销实现强表达力模型更新的能力。本文系统综述并比较了传统PEFT方法与QAA框架，揭示了在收敛性、效率和表示容量之间的权衡，并为未来大模型适应中量子方法的应用提供洞见。
原文摘要 · Abstract (English)
The rapid progress of large language models (LLMs) has transformed natural language processing, yet the challenge of efficient adaptation remains unresolved. Full fine-tuning achieves strong performance but imposes prohibitive computational and memory costs. Parameter-efficient fine-tuning (PEFT) strategies, such as low-rank adaptation (LoRA), Prefix tuning, and sparse low-rank adaptation (SoRA), address this issue by reducing trainable parameters while maintaining competitive accuracy. However, these methods often encounter limitations in scalability, stability, and generalization across diverse tasks. Recent advances in quantum deep learning introduce novel opportunities through quantum-inspired encoding and parameterized quantum circuits (PQCs). In particular, the quantum-amplitude embedded adaptation (QAA) framework demonstrates expressive model updates with minimal overhead. This paper presents a systematic survey and comparative analysis of conventional PEFT methods and QAA. The analysis demonstrates trade-offs in convergence, efficiency, and representational capacity, while providing insight into the potential of quantum approaches for future LLM adaptation.
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