Research Projects:
Introduction:


We are currently focusing on develop the stack for quantum machine learning. The first work, QuantumFlow, is published at Nature Communications. This work is also invited to give contribution talk at ESWEEK’21 and Quantum Weeek’21. Details please visit QuantumFlow.

Members:

Weiwen Jiang and Ph.D. Students in JQub

Publications:
1. Is Quantum Computing Ready for Deep Learning?
Weiwen Jiang, Jinjun Xiong, Jason Cong, and Yiyu Shi
Under Review, Nature Electronics
2. Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow [arXiv]
Z. Liang, Z. Wang, J. Yang, L. Yang, J. Xiong, Y. Shi, W. Jiang,
Accepted by IEEE/ACM International Conference On Computer-Aided Design (ICCAD), Virtual, 2021. (Invited paper)
3. Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs [arXiv]
Z. Wang, Z. Liang, S. Zhou, C. Ding, J. Xiong, Y. Shi, W. Jiang,
Accepted by IEEE/ACM International Conference On Computer-Aided Design (ICCAD), Virtual, 2021. (Invited paper)
4. A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
W. Jiang, J. Xiong, and Y. Shi
Nature Communications, 12, 579, 2021 [NCOMMS] [arXiv];
News: [AI era news-1]; [AI era news-2]
5. When Machine Learning Meets Quantum Computers: A Case Study [arXiv]
W. Jiang, J. Xiong, and Y. Shi
in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), (Invited paper)