50 citations · 72 across the 17 of their papers we have counts for
23 papers
AQM: A Refresh of the Abstract Qubit Model for Quantum Computing Co-design
Chenxu Liu, Samuel A. Stein, Muqing Zheng +2
Qubits are the fundamental building blocks of quantum information science and applications, whose concept is widely utilized in both quantum physics and quantum computation. While…
FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators
Xinyi Li, Ang Li, Bo Fang +3
NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high perfor…
A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein +5
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constrain…
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Rong Dai, Yonggang Zhang, Ang Li +3
One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server mode…
Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases
Jie Yin, Ang Li, Wei Xi +2
We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effectiv…
QuApprox: A Framework for Benchmarking the Approximability of Variational Quantum Circuit
Jinyang Li, Ang Li, Weiwen Jiang
Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data.…