HONG-YE HU, PH.D.
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Theoretical Physicist working on
artificial intelligence,
quantum information,
​and many-body physics.
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"To learn, read. To know, write, To master, teach" 
--- Hindu proverb

About me

​    I am an incoming HQI Fellow at Harvard University supported by Harvard Quantum Initiative and Prof. Susanne Yelin. I am expect to get my PhD in Physics at University of California, San Diego in February 2022. I worked with a brilliant young professor Dr. Yi-Zhuang You since 2018. My current interest involves quantum information, machine learning, and quantum many-body physics.
     I previously have worked at NASA Quantum AI Lab, Salk Institute for Biological Studies, Peking University, and Stanford University.
    My research interest lies in the intersection among quantum information theory, machine learning, and quantum many-body physics. In particular, I am interested in the connection between quantum physics and (quantum) machine learning, such as how to efficiently encode quantum information as classical data,  what quantum properties can or cannot be efficiently learned by classical machines.
    I am also interested in the entanglement properties in many-body physics and its implication on machine learning. In addition, I put those understandings to design efficient algorithms to simulate quantum systems. I also plan to extend my research to quantum computation and quantum algorithms using the ideas and tools from machine learning and quantum information theory.

Honors:

  • Fellow of Harvard Quantum Initiative. (2022-Present)
  • Nominee of UC's President Dissertation Year Fellow by Physics Department. (2021)
  • Chair's Challenge Award recipient, UCSD Physics Department. (2018)
  • Honor title: ​Weiming scholar, Peking University. (2013-2016)
  • Honor title: College Graduate Excellence Award of Beijing City. Ministry of Education. (2016)
  • Gold Medal, China Undergraduate Physics Tournament, Peking University Team (2013)
  • The Chinese Physics Olympiad(CPhO): Gold Medal (2011) 
 Current research interest:
    1) Quantum information/computation using NISQ machine
    2) Classical shadow tomography, and classical representation of quantum data

    3) Quantum dynamics, and open quantum dynamics
    4) Machine learning meets quantum information
    5) Machine learning assists quantum design
    6) Random quantum spin system, and infinite randomness critical point

News & Spotlight

  • [Jan 2022] I am very honored to be selected as HQI Fellow by Harvard Quantum Initiative!
  • [Dec 2021] My paper with Friends Chenhua Geng and Yijian Zou on "Differential Programming of Isometric Tensor Network" has been accepted by Machine Learning Science & Technology with IOP publisher!
  • [July 2021] My paper with Soonwon Choi and Yi-Zhuang on "Classical Shadow Tomography with locally scrambled quantum dynamics" is on arXiv! Surprisingly, we found locally scrambled dynamics can be used for tomography of quantum states, and it brings promising applications to the NISQ machines!
  • [Feb 2021] My paper with Yi-Zhuang "Hamiltonian-Driven Shadow Tomography of Quantum States" has been on the arXiv! And I am very excited to wrote a blog about quantum state tomography and shadow tomography about quantum states. [Blog]
  • [Feb 2021] Our paper "Phase-fluctuation Induced Time-Reversal Symmetry Breaking Normal State" has been on the arXiv!
  • [Oct 2020] Our paper "RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior" has been on the arXiv! Code and visualization can be found here. 
  • [Sep 2020] Our paper "Neural ODE and holographic QCD" has been accepted by Machine Learning Science & Technology!  This new method shed light to explore holographic duality and solve strongly correlated system in a holographic manner. 
  • [June 2020] Our paper "Topological and symmetry-enriched random quantum critical points" has been posted on arXiv! An interplay between strong disorder, critical point, and topological phase. (Aug. 2020)
  • Our paper "Machine Learning Holographic Mapping by Neural Network Renormalization Group" has been accepted by Phys. Rev. Research. A lot more about Neural RG needs to be explored! And it has been featured on UCSD Physical Science News.
  • [May 2020] Our paper "Quantum Magnetism in Wannier-Obstructed Mott Insulators" has been posted on arXiv!  It is a quite exciting work, since Shang Liu is my good friend since High school, back to when we compete in physics olympics. 
  • [April 2020] My paper with Prof. Frank Wilczek (2004 Nobel Laureate) and Prof. Biao Wu titled "Resonant Quantum Search with Monitor Qubits" has been published as Express Letter on Chinese Physics Letter. We have this simple idea of constructing quantum search algorithm using resonance 4 years ago when I was an undergraduate. Finally, we wrote it together. And Prof. Wilczek gave a nice introduction video here. 

Collaborators:

1. Prof. Yi-Zhuang You (UCSD, USA)
2. Prof. Lei Wang (Chinese Science Academy, China)
3. Prof. Koji Hashimoto (Kyoto University, Japan)
4. Prof. Soonwon Choi (MIT, USA)
5. Prof. Frank Wilczek (MIT, USA)
6. Prof. Romain Vasseur (UMass, USA)
7. Prof. Vedika Khemani (Stanford, USA)
8. Prof. Bruno Olshausen (Berkeley, USA)
9. Prof. Congjun Wu (Westlake University, USA)
10. Prof. Biao Wu (Peking University, China)
11. Dr. Xun Gao (Harvard, USA)
12. Dr. Zhihui Wang (NASA Quantum AI Lab, USA)
13. Dr. Yubei Chen (Facebook AI Research)
14. Dr. Shang Liu (Harvard, USA)
15. Dr. Ruben Verresen (Harvard, USA)
16. Dr. Lun-Hui Hu (Penn State Univ. USA)
17. Dr. Shuo-Hui Li (HKST, China)
18. Mr. Eric Anschuetz (MIT, USA)
19. Mr. Dian Wu (EPFL, Switzerland)
20. Mr. Jinlong Huang (UCSD, USA)
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