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Yuxuan Du (杜宇轩)

I am currently a Senior Researcher at JD Explore Academy, and also a member of DMT (Doctor Management Trainee) program in JD.com, Inc. I received my Ph.D. degree in the School of computer science, The University of Sydney, supervised by Prof. Dacheng Tao (IEEE Fellow) and co-supervised by Prof. Min-hsiu Hsieh and Prof. Tongliang Liu. Prior to that, I studied Physics (National Base-level physics class) at Sichuan University. I was a visiting student at University at Albany-State University of New York in 2013.

  duyuxuan123@gmail.com                               Google Scholar

Research Interest

My research interests include quantum machine learning applications, quantum learning theory, and other classical machine learning topics, including but not limited to physics-informed data mining and private learning, and AI for science.


Recruit

I'm recruiting self-motivated employees and interns who have strong coding skills and impressive research background to work with me on quantum machine learning. Welcome to send me your detailed resume!


News

  • [2022/06] We released our new review arcticle "Recent Advances for Quantum Neural Networks in Generative Learning" [PDF].
  • [2022/06] We released our new paper "MSR: Making Self-supervised learning Robust to Aggressive Augmentations" [PDF].
  • [2022/05] I gave an invited talk "Exploring Variational Quantum Algorithms via Statistical Learning Theory" at Institute of Theoretical Physics, Chinese Academy of Sciences [Link].
  • [2022/05] We released our new paper "QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines" [PDF].
  • [2022/05] Our paper "Quantum architecture search for variational quantum algorithms " was accepted by Nature Parter Journal Quantum Information (npj qi) [Link].
  • [2022/05] Our paper "A distributed learning scheme for variational quantum algorithms" was accepted by Transactions on Quantum Engineering [Link].
  • [2022/05] We released our new paper "Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy" [PDF]
  • [2022/04] We released our new paper "Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning" [PDF]
  • [2022/04] I gave an invited talk "Anchoring Variational Quantum Algorithms via Statistical Learning Theory" at Tsinghua-BIMSA [Link].
  • [2022/04] I accepted the invitation to serve as a Reviewer for Neurips 2022 .
  • [2022/03] We released our new paper "Unentangled quantum reinforcement learning agents in the OpenAI Gym" [PDF]
  • [2022/03] I gave an invited talk "Variational Quantum Computing & Learning Theory" at Center on Frontiers of Computing Studies, Peking University [Link].
  • [2022/03] Our paper "Quantum differentially private sparse regression learning " was accepted by Transactions on Information Theory (TIT) [Link].
  • [2022/03] Our paper "DyRep: Bootstrapping Training with Dynamic Re-parameterization " was accepted by CVPR [PDF].
  • [2022/02] Our paper "Efficient bipartite entanglement detection scheme with a quantum adversarial solver " was accepted by Physical Review Letter (PRL) [PDF].
  • [2022/02] Pennylane has collected our proposal of QuGAN [PDF] into their Demos list.
  • [2022/02] I accepted the invitation to serve as a Reviewer for ACMM 2022 .
  • [2022/02] Our paper "Efficient measure for the expressivity of variational quantum algorithms " was accepted by Physical Review Letter (PRL) [PDF].
  • [2022/01] We released our new paper "Do We Need to Penalize Variance of Losses for Learning with Label Noise?" [PDF].
  • [2022/01] We released our new paper "Quantum circuit architecture search on a superconducting processor" [PDF].
  • [2021/12] I accepted the invitation to serve as a Reviewer for ICML 2022 .
  • [2021/12] I accepted the invitation to serve as a Reviewer for IJCAI 2022 .
  • [2021/10] Our paper "On the learnability of quantum neural networks " was accepted by PRX Quantum [PDF].
  • [2021/10] Our paper "Towards understanding the power of quantum kernels in the NISQ era " was accepted by (Quantum Techniques in Machine Learning 2021) as a talk [Website].
  • [2021/08] Our paper "Towards understanding the power of quantum kernels in the NISQ era " was accepted by Quantum [PDF].
  • [2021/08] Our paper "Experimental quantum generative adversarial networks for image generation " was accepted by PRApplied [Editor Suggestion] [PDF].
  • [2021/07] I accepted the invitation to serve as a Reviewer for ICLR 2022 .
  • [2021/07] We organize a course "Adanced topics of AI" at School of Gifted Young, USTC.
  • [2021/06] We released our new paper "On exploring practical potentials of quantum auto-encoder with advantages" [PDF].
  • [2021/06] We released our new paper "Accelerating variational quantum algorithms with multiple quantum processors" [PDF].
  • [2021/06] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at CCF forum (第二届CCF理论计算机科学全国优秀博士生论坛) [Website Link].
  • [2021/06] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at University of Electronic Science and Technology of China.
  • [2021/06] I accepted the invitation to serve as a Reviewer for NeurIPS 2021 .
  • [2021/05] We released our new paper "The dilemma of quantum neural networks" [PDF][Source code].
  • [2021/05] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at Beihang University.
  • [2021/05] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at Beijing Academy of Quantum Information Sciences (BAQIS).
  • [2021/04] I accepted the invitation to serve as a Reviewer for ACML 2021.
  • [2021/04] We released our new paper "An efficient measure for the expressivity of variational quantum algorithms" [PDF].
  • [2021/04] Our paper "Quantum noise protects quantum classifiers against adversaries" was accepted by PRR [Arxiv].
  • [2021/04] I gave an invited talk "The Capabilities and Limitations of Quantum Learning Models" at Center on Frontiers of Computing Studies, Department of Computer Science, Peking University. Slides is avaiable at here.
  • [2021/04] I accepted the invitation to serve as a Reviewer for ACMM 2021 .
  • [2021/04] We released our new paper "Towards understanding the power of quantum kernels in the NISQ era" [PDF].

Publications

2022

Quantum architecture search for variational quantum algorithms
Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, Dacheng Tao
10.1038/s41534-022-00570-y (npj Quantum Information), 2022
[PDF]

A distributed learning scheme for variational quantum algorithms
Yuxuan Du, Yang Qian, Xingyao Wu, Dacheng Tao
10.1109/TQE.2022.3175267 (TQE), 2022
[PDF]

Quantum differentially private sparse regression learning
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao
TIT.2022.3164726 (TIT), 2022
[PDF]

Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver
Xu-Fei Yin*, Yuxuan Du*, Yue-Yang Fei*, Rui Zhang, Li-Zheng Liu, Yingqiu Mao, Tongliang Liu, Min-Hsiu Hsieh, Li Li, Nai-Le Liu, Dacheng Tao, Yu-Ao Chen, and Jian-Wei Pan ('*' stands for co-authors)
PhysRevLett.128.110501 (PRL), 2022
[PDF]

DyRep: Bootstrapping Training with Dynamic Re-parameterization
Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
[PDF]

Efficient measure for the expressivity of variational quantum algorithms
Yuxuan Du, Zhuozhuo Tu, Xiao Yuan, Dacheng Tao
PhysRevLett.128.080506 (PRL), 2022
[PDF] A brief summary [Link]

2021

Learnability of quantum neural networks
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao
PRXQuantum.2.040337 (PRX Quantum), 2021
[PDF]

Towards understanding the power of quantum kernels in the NISQ era
Xinbiao Wang*, Yuxuan Du*, Yong Luo, Dacheng Tao (Under my supervision, '*' stands for co-authors)
Quantum 5, 531 (2021) (Quantum), 2021
[PDF]

Experimental quantum generative adversarial networks for image generation
He-liang Huang*, Yuxuan Du*, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, Rui Yang, Tongliang Liu, Min-Hsiu Hsieh, Hui Deng, Hao Rong, Cheng-Zhi Peng, Chao-Yang Lu, Yu-Ao Chen, Dacheng Tao, Xiaobo Zhu, Jian-Wei Pan ('*' stands for co-authors)
Phys. Rev. Applied 16, 024051 (PRApplied, Editor Suggestion), 2021
[PDF]
Hands-on tutorial is provided by Pennylane [Demo]

Quantum noise protects quantum classifiers against adversaries
Yuxuan Du, Min-hsiu Hsieh, Tongliang Liu, Dacheng Tao, Nana Liu
Phys. Rev. Research 3, 023153 (PRR), 2021
[PDF]

A Grover-search based quantum learning scheme for classification
Yuxuan Du, Min-hsiu Hsieh, Tongliang Liu, Dacheng Tao
New Journal of Physics (NJP), 2021
[PDF]

2020

Quantum-inspired algorithm for general minimum conical hull problems
Yuxuan Du, Min-hsiu Hsieh, Tongliang Liu, Dacheng Tao
Phys. Rev. Research 2, 033199 (PRR), 2020
[PDF]

Expressive power of parametrized quantum circuits
Yuxuan Du, Min-hsiu Hsieh, Tongliang Liu, Dacheng Tao
Phys. Rev. Research 2, 033125 (PRR), 2020
[PDF]

2018

Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization
Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), 2018
[PDF]

2017

New insights on the magnetic properties of ferromagnetic FePd3 single-crystals encapsulated inside carbon nanomaterials
Filippo S Boi, Yuxuan Du , Sameera Ivaturi, Yi He, Shanling Wang
Materials Research Express, 2017
[PDF]


Manuscripts

Recent Advances for Quantum Neural Networks in Generative Learning
Jinkai Tian, Xiaoyu Sun, Yuxuan Du †,Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu †, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang †, Dacheng Tao † (Under my supervision, '†' stands for corresponding author)
[Arxiv]

MSR: Making Self-supervised learning Robust to Aggressive Augmentations
Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du †, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu
[Arxiv]

QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines
Zeqiao Zhou, Yuxuan Du †, Xinmei Tian †, Dacheng Tao † (Under my supervision, '†' stands for corresponding author)
[Arxiv]

Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy
Yuxuan Du, Zhuozhuo Tu, Bujiao Wu, Xiao Yuan, Dacheng Tao
[Arxiv]

Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
Qiuhao Chen, Yuxuan Du †, Qi Zhao, Yuling Jiao, Xiliang Lu, Xingyao Wu † (Under my supervision, '†' stands for corresponding author)
[Arxiv]

Unentangled quantum reinforcement learning agents in the OpenAI Gym
Jen-Yueh Hsiao, Yuxuan Du , Wei-Yin Chiang, Min-Hsiu Hsieh, Hsi-Sheng Goan
[Arxiv]

Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Yexiong Lin, Yu Yao, Yuxuan Du , Jun Yu, Bo Han, Mingming Gong, Tongliang Liu
[Arxiv]

Quantum circuit architecture search on a superconducting processor
Kehuan Linghu, Yang Qian, Ruixia wang †, Meng-Jun Hu, Zhiyang Li, Xuegang Li, Huikai Xu, Jingning Zhang, Teng Ma, Peng Zhao, Dong E. Liu, Min-Hsiu Hsieh, Xingyao Wu †, Yuxuan Du †, Dacheng Tao †, Yirong Jin, Haifeng Yu (Under my supervision, '†' stands for corresponding author)
[Arxiv]

On exploring practical potentials of quantum auto-encoder with advantages
Yuxuan Du, Dacheng Tao
[Arxiv]

The dilemma of quantum nerual networks
Yang Qian, Xinbiao Wang, Yuxuan Du, Xingyao Wu, Dacheng Tao (Under my supervision)
[Arxiv] [Source code]


Academic Services

Reviewer for Conferences:
International Conference on Machine Learning (ICML, 2022), International Conference on Learning Representations (ICLR, 2022), Conference on Neural Information Processing Systems (NeurIPS, 2021, 2022), International Joint Conferences on Artificial Intelligence (IJCAI, 2021, 2022), ACM Multimedia (ACMM, 2021, 2022), Asian Conference on Machine Learning (ACML, 2021), Conference on Quantum Information Processing (QIP, 2021), Asian Quantum Information Science Conference (AQIS, 2020, 2021)

Reviewer for Journals:
Physical Review X Quantum (PRX Quantum), Physical Review A (PRA), Physical Review Research (PRR), IOP Science Machine Learning: Science and Technology (MLST), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Quantum Engineering (TQE), Frontiers of Physics, Chinese Physics Letters (CPL), SciPost Physics


Group members

Xinbiao Wang (Wuhan University, 2021)
Yang Qian (The University of Sydney, 2021)
Zeqiao Zhou (University of Science and Technology of China, 2021)
Qiuhao Chen (Wuhan University, 2021)


Selected Awards

[2018] Engineering and Information Technologies Research Scholarship, University of Sydney
[2014] Scholarship awarded by China Scholarship Council for study aboard to the US