4 papers
Generation via Classical Noise Reuploading
Xin Wang, Rebing Wu
We propose a novel quantum generative model paradigm that fundamentally avoids the issue of extremely small post-selection probabilities present in previous models. Unlike existing…
Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks
Jie Liu, Xin Wang
Variational quantum algorithms are promising for near-term quantum computing, but are severely limited by hardware noise and the substantial circuit overhead required for error mit…
On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
Han-Xiao Tao, Xin Wang, Re-Bing Wu
Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications…
Predictive Performance of Deep Quantum Data Re-uploading Models
Xin Wang, Han-Xiao Tao, Re-Bing Wu
Quantum machine learning models incorporating data re-uploading circuits have garnered significant attention due to their exceptional expressivity and trainability. However, their…