1 citations · 3 across the 5 of their papers we have counts for
5 papers
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Cheng Jiang, Sitian Qian, Kevin Pedro +3
High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for t…
Distilling the knowledge with quantum neural networks
Yuxuan Yan, Sitian Qian, Qi Zhao +1
Quantum Neural Networks (QNNs) are a promising class of quantum machine learning models with potential quantum advantages when implemented on scalable, error-corrected quantum comp…
Application of Structured State Space Models to High energy physics with locality-sensitive hashing
Cheng Jiang, Sitian Qian
Modern high-energy physics (HEP) experiments are increasingly challenged by the vast size and complexity of their datasets, particularly regarding large-scale point cloud processin…
BUFF: Boosted Decision Tree based Ultra-Fast Flow matching
Cheng Jiang, Sitian Qian, Huilin Qu
Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimension…
Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
Cheng Jiang, Sitian Qian, Huilin Qu
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks.…