Lu Wei | 韦璐

A world for physics and mathematics

ABOUT ME :

I am currently a second year PhD student in Department of Data Science at Stony Brook University, supervised by Prof. Haibin Ling. Previously I received my B.S. degree from University of Science and Technology of China with a major in Physics. My research focuses on quantum machine learning and AI4S.

PERSONAL INFORMATION

📧 Email: lu.wei.1 AT stonybrook.edu
🔬 Major: Physics and Data Science
📝 Curriculum Vitae: link

RESEARCH INTEREST

Quantum nonlocality
Quantum foundation
Quantum machine learning
AI4S

MISCELLANEOUS

I enjoy hiking through scenic trails and spending time in parks.

RESEARCH :

📖 Publications and preprints:

click on title to view abstract

• Entanglement area law for shallow and deep quantum neural network states (Zhih-Ahn Jia, Lu Wei, Yu-Chun Wu, Guang-Can Guo, Guo-Ping Guo; New Journal of Physics, Volume 22, May 2020)	arXiv:1907.11333
A study of the artificial neural network representation of quantum many-body states is presented. The locality and entanglement properties of states for shallow and deep quantum neural networks are investigated in detail. By introducing the notion of local quasi-product states, for which the locally connected shallow feed-forward neural network states and restricted Boltzmann machine states are special cases, we show that Rényi entanglement entropies of all these states obey the entanglement area law. Besides, we also investigate the entanglement features of deep Boltzmann machine states and show that locality constraints imposed on the neural networks make the states obey the entanglement area law. Finally, as an application, we apply the notion of Rényi entanglement entropy to understanding the power of neural networks and show that image classification problems which can be efficiently solved must obey the area law.
• Quantum advantages of communication complexity from Bell nonlocality (Zhih-Ahn Jia, Lu Wei, Yu-Chun Wu, Guang-Can Guo)	arXiv:2004.05098
Communication games are crucial tools for investigating the limitations of physical theories. The inverse communication complexity problem is a typical example, for which several distributed parties attempting to jointly calculate a given function with some restricted classical communications. In this work, we present a class of inverse communication complexity problems in a graph-theoretic way, for which by pre-sharing entangled states, the probability for successfully obtaining the values of the given function will exceed the ones for arbitrary classical strategies. We also present a non-signaling protocol based on Popescu-Rohrlich box, whose success probability reaches one. This quantum advantage stems from the fact that quantum correlations are stronger than all classical correlations and non-signaling correlation can be stronger than quantum correlation. Our work gives a complexity-theoretical study of this hierarchy of the strength for different physical theories.

LECTURE NOTES :

I will gradually post my lecture note here, courses include quantum mechanics, statistical mechanics, basic quantum theory, etc.

📓 Quantum mechanics:
• Quantum mechanics lecture notes (01,02)
• Quantum mechanics exercises (homework 05, 2, 3)

📓 Quantum information theory:
• Quantum information lecture notes (01,02)
• Quantum information exercises (exercise 01, exercise 02,exercise 03)

📓 Quantum computation:
• Quantum computation lecture notes (01,02)
• Quantum computation exercises (exercise 01, 2, 3)

📓 Statistical mechanics:
• Statistical mechanics lecture notes (01,02)
• Statistical mechanics exercises (exercise 01, 2, 3)

📓 Neural netowrks:
• Neural network lecture notes (01,02)
• Neural network exercises (exercise 01, 2, 3)

📓 Machine learning:
• Machine learning lecture notes (01,02)
• Machine learning exercises (exercise 01, 2, 3)

MISCELLANEA :

This part is left for miscellaneous stuffes.