QCHFT: Quantum Cross-Hybrid Fine-Tuning for LLMs
Abstract: When full-parameter updates are impractical for large language models (LLMs), parameter-efficient fine-tuning (PEFT) is commonly employed to reduce the number of trainable parameters. Classical PEFT methods, such as low-rank adaptation (LoRA), are limited to linear transformations and may not capture complex, high-order feature interactions under stringent parameter constraints. In contrast, parameterized quantum circuits (PQCs) […]