About
I am Zhide Lu, a Ph.D. student advised by Dong-Ling Deng at Tsinghua University. I will receive my Ph.D. 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
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:
Expressibility-induced concentration of quantum neural tangent kernels
Li-Wei Yu$\dagger$, Weikang Li, Qi Ye, Zhide Lu, Zizhao Han, and Dong-Ling Deng$\dagger$
Deep quantum neural networks on a superconducting processor
Xiaoxuan Pan, Zhide Lu(co-first author), Dong-Ling Deng$\dagger$, Luyan Sun$\dagger$, et al.
Quantum neural network classifiers: A tutorial
Weikang Li, Zhide Lu, Dong-Ling Deng$\dagger$
Quantum continual learning overcoming catastrophic forgetting
Wenjie Jiang, Zhide Lu, Dong-Ling Deng$\dagger$
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)
Markovian Quantum Neuroevolution for Machine Learning
Zhide Lu, Pei-Xin Shen (co-first author), Dong-Ling Deng$\dagger$
Talks & Conferences:
12th International Conference on Computing and Pattern Recognition (2023)
Invited talk: Recent Progress on Deep Quantum Neural Networks
APS March Meeting 2023, Virtual
Contributed talk: Markovian Quantum Neuroevolution for Machine Learning
PIERS2023 (Prague, Czech), Session “Quantum Computation and Quantum Simulation” (2023)
Contributed talk: Deep Quantum Neural Networks on a Superconducting Processor
The 2nd International Conference on Emerging Quantum Technology (2023)
Poster: Deep quantum neural networks on a superconducting processor
The 6-th International Conference on “Quantum Information, Spacetime, and Topological Matter” (2021)
Poster: Markovian Quantum Neuroevolution for Machine Learning