Quarl: A Learning-Based Quantum Circuit Optimizer

no code implementations • 17 Jul 2023 • Zikun Li, Jinjun Peng, Yixuan Mei, Sina Lin, Yi Wu, Oded Padon, Zhihao Jia

Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation.

Reinforcement Learning (RL)

Redundancy-Free Computation Graphs for Graph Neural Networks

no code implementations • 9 Jun 2019 • Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken

Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.

Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks

no code implementations • ICML 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.

Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

no code implementations • 14 Feb 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.

Exploring the Hidden Dimension in Accelerating Convolutional Neural Networks

no code implementations • ICLR 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.