5 papers
Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
Kaiyang Xing, Han Fang, Zhaoyun Chen +4
Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fi…
Q-Tag: Watermarking Quantum Circuit Generative Models
Yang Yang, Yuzhu Long, Han Fang +4
Quantum cloud platforms have become the most widely adopted and mainstream approach for accessing quantum computing resources, due to the scarcity and operational complexity of qua…
QPanda3: A High-Performance Software-Hardware Collaborative Framework for Large-Scale Quantum-Classical Computing Integration
Tianrui Zou, Yuan Fang, Jing Wang +8
In emerging quantum-classical integration applications, the classical time cost-especially from compilation and protocol-level communication often exceeds the execution time of qua…
Quantum-Enhanced LLM Efficient Fine Tuning
Xiaofei Kong, Lei Li, Zhaoyun Chen +10
Low-Rank Adaptation (LoRA) enables efficient fine-tuning of pre-trained language models through low-rank matrix approximation, achieving effectiveness in many scenarios. However, i…
QSRA: A QPU Scheduling and Resource Allocation Approach for Cloud-Based Quantum Computing
Binhan Lu, Zhaoyun Chen, Yuchun Wu
Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a…