Quantum-Enhanced LLM Efficient Fine Tuning

Low-Rank Adaptation (LoRA) enables efficient fine-tuning of pre-trained language models via low-rank matrix approximation, which is effective in many scenarios. However, its low-rank representation capacity is constrained in complex tasks or high-rank dependency settings, potentially limiting model adaptability. Addressing the expressive bottleneck of classical low-rank approximation in fine-tuning large language models, this paper proposes a parameter-efficient fine-tuning method based on a Quantum Weighted Tensor Hybrid Network (QWTHN), which leverages Quantum Neural Network (QNN). The study investigates quantum-classical hybrid parameter-efficient fine-tuning in low-rank spaces. QWTHN decomposes pre-trained weights into quantum neural network and tensor network representations, utilizing quantum state superposition and other methods to break through classical rank limitations. Experiments show that the proposed quantum fine-tuning technique for large models approaches or even surpasses the parameter efficiency of LoRA. On the CPsyCounD and R1-Distill-SFT datasets, QWTHN, compared to classical LoRA, reduces training loss by up to 15% while using 76% fewer parameters, and achieves an 8.4% performance improvement on the CPsyCounD test set. This research not only realizes lightweight and efficient adaptation of quantum resources to billion-parameter models but also validates the practical path of quantum hardware driven by large model tasks, laying the first engineering-ready technical foundation for future quantum-enhanced AGI systems.

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.