no code implementations • 26 Nov 2021 • Anbang Wu, Gushu Li, yuke wang, Boyuan Feng, Yufei Ding, Yuan Xie
In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing.
no code implementations • 25 Nov 2021 • Anbang Wu, Gushu Li, Yufei Ding, Yuan Xie
In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing.
1 code implementation • 11 Jun 2020 • Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, Yufei Ding
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings).
Distributed, Parallel, and Cluster Computing
no code implementations • 28 Nov 2019 • Gushu Li, Li Zhou, Nengkun Yu, Yufei Ding, Mingsheng Ying, Yuan Xie
In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer.
no code implementations • 26 Aug 2019 • Yuke Wang, Boyuan Feng, Gushu Li, Lei Deng, Yuan Xie, Yufei Ding
As a promising solution to boost the performance of distance-related algorithms (e. g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges.
Distributed, Parallel, and Cluster Computing Programming Languages
no code implementations • International Conference on Architectural Support for Programming Languages and Operating Systems 2019 • Gushu Li, Yufei Ding, Yuan Xie
Due to little consideration in the hardware constraints, e. g., limited connections between physical qubits to enable two-qubit gates, most quantum algorithms cannot be directly executed on the Noisy Intermediate-Scale Quantum (NISQ) devices.
