reviews: 
Comments: The paper introduces Quantum Parameter Adaptation (QPA), a novel approach for parameter-efficient learning that combines quantum neural networks (QNNs) with classical machine learning to generate parameters for fine-tuning large models. The QPA method utilizes quantum circuits to generate classical model weights, allowing for significant parameter 
reduction without compromising performance, as demonstrated with Gemma-2 and GPT-2 case studies. The method enables practical use of quantum machine learning by decoupling quantum hardware requirements from the inference phase, thus making inference feasible on classical systems. The results show that QPA can effectively reduce the number of parameters required for methods like Low-Rank Adaptation (LoRA), while maintaining or improving model performance, indicating its potential as a scalable and efficient quantum-classical hybrid solution for large-scale learning tasks.
Strengths: 1. The paper proposes the Quantum Parameter Adaptation (QPA) method, which is a new method that combines  quantum neural networks (QNN) with classical multi-layer perceptrons to generate The parameters of the fine-tuning method demonstrate the efficiency in parameter reduction. 
2. QPA separates quantum computing from the inference stage, allowing the model to perform inference without  relying on quantum hardware, greatly reducing deployment complexity and thus having practical application value for classical hardware inference. 
3. Experimental results show that QPA can significantly reduce the number of parameters while maintaining or slightly improving model performance. In contrast, methods such as LoRA, DoRA,  and Prefix Tuning (PT) are highly efficient in parameters. The performance in learning tasks is not as good as QPA, highlighting its high efficiency. 
4. The authors extend the QPA method to large models such as GPT-2 and Gemma-2, significantly expanding the scope of previous quantum parameter generation studies. 
5. QPA allows flexible exploration of different parameter configurations, improving its 
adaptability in different models and parameter efficient fine-tuning (PEFT) methods.
Weaknesses: 1. The study assumes that the measurement probability is exact and does not consider noise, which may not accurately reflect the situation where a finite number of measurements are required in the actual quantum hardware environment. Although the impact of this limitation on performance is discussed, the problem is not completely solved. It is possible to consider introducing some simulated noise models to verify the robustness of the proposed QPA.
2. The paper mentions that QPA uses RY and CNOT gates to build quantum circuit ansatz, but lacks a detailed explanation of why this ansatz structure is chosen. It is recommended that the author add a detailed argument for the choice of ansatz and compare the effects of different quantum gate combinations to ensure the rationality of the current choice.
3. The method uses the gradient obtained from quantum measurements, which may introduce additional complexity in the training process, especially when implemented on actual quantum hardware, due to the presence of parameter offsets and measurement noise.
4. The paper mentions that the theoretical analysis of the convergence behavior, trainability and learnability characteristics of QPA will be addressed in future research, which shows that there is a gap in the current understanding of the long-term effectiveness of QPA.
5. More shortcomings can be found in the Questions section.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3