About

I am Zhide Lu, a research scientist in Shanghai QiZhi Institute. I obtained my Ph.D. at Tsinghua University advised by Dong-Ling Deng in 2024.

Research interest:

My research focuses on the interplay between quantum information, artificial intelligence (AI), and quantum physics. On the one hand, a range of tools and ideas from AI can be applied to solve complex quantum problems. On the other hand, quantum computing also brings unprecedented opportunities to enhance or innovate AI algorithms. My specific research directions include:

Quantum-enhanced machine learning

• Designing new quantum machine learning algorithms that offer quantum speed-ups over their classical counterparts
• Finding machine learning problems that show unambitious complexity separation between quantum and classical algorithms
• developing theoretical and empirical characterization of new classes of quantum machine learning models

Machine learning for quantum physics

• Solving quantum many-body problems, such as finding the system’s ground state and simulating its dynamics
• Developing explainable, trustworthy machine learning methods
• Developing efficient methods that search for the quantum circuit architectures

Publications:

1. Non-Hermitian Persistent Current Transport

   Pei-Xin Shen, Zhide Lu, Jose L Lado, Mircea Trif

   arXiv:2403.09569 (2024)

2. Long-lived topological time-crystalline order on a quantum processor

   Liang Xiang, Wenjie Jiang, Zehang Bao, Zixuan Song, Shibo Xu, Ke Wang, Jiachen Chen, Feitong Jin, Xuhao Zhu, Zitian Zhu, Fanhao Shen, Ning Wang, Chuanyu Zhang, Yaozu Wu, Yiren Zou, Jiarun Zhong, Zhengyi Cui, Aosai Zhang, Ziqi Tan, Tingting Li, Yu Gao, Jinfeng Deng, Xu Zhang, Hang Dong, Pengfei Zhang, Si Jiang, Weikang Li, Zhide Lu, Zheng-Zhi Sun, Hekang Li, Zhen Wang, Chao Song, Qiujiang Guo, Fangli Liu, Zhe- Xuan Gong, Alexey V Gorshkov, Norman Y Yao, Thomas Iadecola, Francisco Machado, H Wang, Dong-Ling Deng

   arXiv:2401.04333 (2024)

3. Expressibility-induced concentration of quantum neural tangent kernels

   Li-Wei Yu^$\dagger$, Weikang Li, Qi Ye, Zhide Lu, Zizhao Han, and Dong-Ling Deng^$\dagger$

   arXiv:2311.04965 (2023)

4. Deep quantum neural networks on a superconducting processor

   Xiaoxuan Pan, Zhide Lu(co-first author), Dong-Ling Deng^$\dagger$, Luyan Sun^$\dagger$, et al.

   Nature Communications 14, 4006 (2023)

5. Quantum neural network classifiers: A tutorial

   Weikang Li, Zhide Lu, Dong-Ling Deng^$\dagger$

   SciPost Phys. Lect. Notes 61 (2022) $\quad$ code

6. Quantum continual learning overcoming catastrophic forgetting

   Wenjie Jiang, Zhide Lu, Dong-Ling Deng^$\dagger$

   Chinese Phys. Lett. 39 050303 (2022)

7. Adversarial learning in quantum artificial intelligence

   Pei-Xin Shen, Wenjie Jiang, Weikang Li, Zhide Lu, and Dong-Ling Deng^$\dagger$

   Acta Phys. Sin., 2021, 70: 140302 (2021) (Invited review in Chinese)

8. Markovian Quantum Neuroevolution for Machine Learning

   Zhide Lu, Pei-Xin Shen (co-first author), Dong-Ling Deng^$\dagger$

   Phys. Rev. Applied 16, 044039 (2021)

Talks & Conferences:

1. QUANTUMatter 2024 (San Sebastian · Donostia, Spain, 2024)

   Poster: Deep quantum neural networks equipped with the backpropagation algorithm

2. 12th International Conference on Computing and Pattern Recognition (2023)

   Invited talk: Recent Progress on Deep Quantum Neural Networks

3. APS March Meeting 2023, Virtual

   Contributed talk: Markovian Quantum Neuroevolution for Machine Learning

4. PIERS2023 (Prague, Czech), Session “Quantum Computation and Quantum Simulation” (2023)

   Contributed talk: Deep Quantum Neural Networks on a Superconducting Processor

5. The 2nd International Conference on Emerging Quantum Technology (2023)

   Poster: Deep quantum neural networks on a superconducting processor

6. The 6-th International Conference on “Quantum Information, Spacetime, and Topological Matter” (2021)

   Poster: Markovian Quantum Neuroevolution for Machine Learning