6 papers
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…
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…
Continuous-variable Quantum Diffusion Model for State Generation and Restoration
Haitao Huang, Chuangtao Chen, Qinglin Zhao
The generation and preservation of complex quantum states against environmental noise are paramount challenges in advancing continuous-variable (CV) quantum information processing.…
Quantum Complex-Valued Self-Attention Model
Fu Chen, Qinglin Zhao, Li Feng +3
Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete…
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…
Quantum Mixed-State Self-Attention Network
Fu Chen, Qinglin Zhao, Li Feng +3
Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Q…