Kati8/ICLR_2023_2025 huggingface dataset:
authors: Chen-Yu Liu
authors: Hsi-Sheng Goan
authors: Min-Hsiu Hsieh
reviews: 
Comments: The paper presents a novel approach for quantum parameter generation aimed at providing a quantum-classical solution for fine-tuning LLM's parameters.  Authors conduct simulation on benchmark datasets to assess performance compared to traditional fine-tuning techniques like LoRA and DoRA. Results indicate improvements in reducing the number of parameters while maintaining or surpassing the performance of existing PEFT methods.
Strengths: The authors introduce an new framework that aims to bridge the gap between classical machine learning and quantum computing.
Weaknesses: 1. Efficiency Concerns: The proposed quantum parameter generation method, while designed to reduce the need for quantum hardware during inference, still relies heavily on quantum neural networks (QNN) during training. This raises critical questions regarding the efficiency of the overall process. Specifically, the optimization of quantum neural network parameters is prone to challenges such as barren plateaus, which can significantly hinder the training process. The authors do not sufficiently address how these issues may impact the efficiency of fine-tuning parameters of classical large models, particularly given the complexities associated with high-dimensional optimization landscapes typical of QNN.

2. The inputs of the mapping model necessitates a precise value of the corresponding measurement probabilities of the basis of quantum states. Typically, these probabilities can only be approximated through repeated measurements and averaging, which introduces another layer of complexity. The need for multiple measurements raises concerns regarding the time overhead involved. The paper does not adequately discuss the potential delays that may arise from extensive measurement requirements, especially in the context of noisy intermediate-scale quantum (NISQ) devices where measurement fidelity can be compromised by inherent noise. Furthermore, the implications of limited measurement shots on the accuracy of estimated probabilities and the subsequent effect on LLM's fine-tuning are not sufficiently explored.

Although the authors mention the limitations of finite measurement shots in Appendix B, the reviewer believes that providing sufficient experimental results, including finite measurements and noise-affected simulations, is essential for demonstrating the feasibility of the proposed method in the paper.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: The paper proposed quantum parameter adaptation (QPA), a method that combines quantum systems with classical models to enhance parameter-efficient fine-tuning in machine learning, especially for LLMs like GPT-2 and Gemma-2. Unlike traditional quantum machine learning methods that require quantum hardware for both training and inference, this approach uses quantum resources exclusively during the training phase to generate parameters for classical models, eliminating the need for quantum hardware at inference. This design enhances scalability and resource efficiency on classical hardware, addressing practical deployment concerns.
Strengths: QPA achieves significant reductions in the number of trainable parameters required for fine-tuning LLMs while maintaining performance, making it highly relevant for parameter-efficient learning. This reduction makes the method resource-efficient and feasible for real-world application. In addition, the paper presents an elegant integration of quantum and classical computing. The authors demonstrate QPA’s effectiveness on state-of-the-art LLMs, contributing meaningfully to the rapidly advancing field of LLM research.
Weaknesses: From my understanding, the quantum component of QPA does not appear to harness the inherent advantages of quantum machine learning. Since the quantum state must be fully characterized before classical post-processing, this process typically requires full quantum state tomography, involving an exponential number of measurements with respect to the number of qubits. This requirement is likely more computationally intensive than classical simulation, eliminating any quantum advantage. In this regard, QPA might better be described as a quantum-inspired algorithm rather than a quantum machine learning algorithm, as it does not appear to yield a speed-up or other quantum advantages over classical simulations. The authors themselves acknowledge in Appendix B that modeling the quantum state using a classical generative model could offer a more practical alternative.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3