2 citations · 2 across the 2 of their papers we have counts for
5 papers
Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
Chuangtao Chen, Qinglin Zhao, MengChu Zhou +3
Discrete diffusion models represent a significant advance in generative modeling, demonstrating remarkable success in synthesizing complex, high-quality discrete data. However, to…
HQViT: Hybrid Quantum Vision Transformer for Image Classification
Hui Zhang, Qinglin Zhao, Mengchu Zhou +1
Transformer-based architectures have revolutionized the landscape of deep learning. In computer vision domain, Vision Transformer demonstrates remarkable performance on par with or…
A Survey of Quantum Transformers: Architectures, Challenges and Outlooks
Hui Zhang, Qinglin Zhao, Mengchu Zhou +4
Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing. Since 2022, research in this area has ra…
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble
Chuangtao Chen, Qinglin Zhao, MengChu Zhou +3
Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing. However,…
A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction
Lingqiang Chen, Qinglin Zhao, Guanghui Li +3
Deep graph convolution networks (GCNs) have recently shown excellent performance in traffic prediction tasks. However, they face some challenges. First, few existing models conside…