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](../../../../categories/QF/).
Members:
Weiwen Jiang and Ph.D. Students in JQub
Publications:
1.
VACSEN: A Visualization Approach for Noise Awareness in Quantum Computing [arXiv]
Shaolun Ruan, Yong Wang, Weiwen Jiang, Ying Mao, Qiang Guan
Work-in-Progress, 2022
2.
Variational Quantum Pulse Learning [arXiv]
Zhiding Liang, Hanrui Wang, Jinglei Cheng, Yongshan Ding, Hang Ren, Zhengqi Gao, Zhirui Hu, Duane S. Boning, Xuehai Qian, Song Han, Weiwen Jiang, Yiyu Shi
Work-in-Progress, 2022
3.
Is Quantum Computing Ready for Deep Learning?
Weiwen Jiang, Jinjun Xiong, Jason Cong, and Yiyu Shi
Under Review, Nature Electronics
4.
Quantum Neural Network Compression [arXiv]
Zhirui Hu, Peiyan Dong, Zhepeng Wang, Youzuo Lin, Yanzhi Wang, Weiwen Jiang
In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022
5.
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)
6.
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)
7.
A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
W. Jiang, J. Xiong, and Y. Shi
8.
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)
Hornors:
