25 citations · 157 across the 25 of their papers we have counts for
4 papers · 1 filter
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow
Zhiding Liang, Zhepeng Wang, Junhuan Yang +4
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error corr…
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
Zhepeng Wang, Zhiding Liang, Shanglin Zhou +3
With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers ar…
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang +5
Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sam…
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
Seung Won Min, Kun Wu, Sitao Huang +5
With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training. However, training GNN…