39 citations · 56 across the 7 of their papers we have counts for
8 papers
ProML: Progressive Multi-label Learning
Yi Kou, Jieting Wang, Yuhua Qian
Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regularization. While empirically ef…
Accelerating Multicanonical Sampling with Irreversibility
Thomas Vogel, Ying Wai Li
Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding gr…
Quantum ensemble learning with a programmable superconducting processor
Jiachen Chen, Yaozu Wu, Zhen Yang +30
Quantum machine learning is among the most exciting potential applications of quantum computing. However, the vulnerability of quantum information to environmental noises and the c…
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Sakib Matin, Emily Shinkle, Yulia Pimonova +5
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Dataset…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
Leveraging junk information to enhance the quantum error mitigation
Ruixia Wang, Xiaosi Xu, Fei Yan +4
Noise in quantum information processing poses a significant obstacle to achieving precise results. Quantum error mitigation techniques are crucial for improving the accuracy of exp…