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Hao Tang received a B.S. degree in Electronic Science and Technology from Huazhong University of Science and Technology in 2010, and a Ph.D. degree in Optoelectronics for Sheffield University (UK) in 2014. She also took an executive program on quantitative finance at London Business School in 2013. She worked as a postdoctoral researcher at Shanghai Jiao Tong University during 2015–2019. She became an assistant researcher in March 2020, and an associate researcher in December 2020. She is now a professor since July 2024. 

Hao Tang dedicates to the experimental and theoretical exploration of quantum computing and quantum simulation on integrated photonics, and the applications of quantum computing for machine learning, finance and optimization tasks. Especially, she and the students, together with collaborators, are making theorecial and experimental efforts on the scalable photonic quantum computing that eventually leads towards fault-tolerant universal photonic quantum computers. Related efforts include the improved quantum photon source, circuit and detectors via advanced semiconductor techniques, the architecture optimization for measurement-based quantum computing and error-correcting protocols for photonic quantum systems.   

Hao Tang received the “Forbes China 30 under 30” award for the sector of sciences in 2019 and the "Shanghai S&T 35U35" award in 2021.

Research Interests:

1. Analog quantum computing and variational quantum computing on integrated photonic chips

2. Theoretical and experimental efforts towards fault-tolerant universal photonic quantum computing 

2. Quantum simulation of open quantum systems, quantum machine learning and quantum optimization

Some on-going funded projects include:

• Photonic quantum chips via heterogeneous integration and high-speed photonic quantum information processing

• Quantum machine learning on NISQ and fault-tolerant quantum devices

• High-performance quantum photon source 

• Quantum information processing on hybrid integrated photonic chips

1. Tang, H.*, Shang, X. W.*, Shi, Z. Y., He, T. S., Feng, Z., Wang, T. Y., Shi, R. X., Wang, H. M., Tan, X., Xu, X. Y., Wang, Y., Gao, J., Kim, M. S. and Jin, X. M.^# Simulating photosynthetic energy transport on a photonic network. npj Quantum Information 10, 29 (2024).

2. Yuan, X. J.*, Chen, Z. Q.*, Liu, Y. D., Xie, Z., Jin, X. M.#, Liu, Y. Z., Wen, X.#, and Tang. H.# Quantum support vector machines for aerodynamic classification. Intelligent Computing 2, 0057 (2023).

3. Tang, H., Banchi, L., Wang, T. Y., Shang, X. W., Tan, X., Zhou, W. H., Feng, Z., Pal, A., Li, H., Hu, C. Q., Kim, M. S. and Jin, X. M.^# Generating Haar-Uniform Randomness Using Stochastic Quantum Walks on a Photonic Chip. Physical Review Letters 12, 050503 (2022).

4. Tang, H.^#, Anurag, P., Wang, T. Y., Qiao, L. F., Gao, J., and Jin, X. M.^# Quantum Computation for Pricing the Collateralized Debt Obligations. Quantum Engineering 3, e84 (2021).

5.Tang, H.*, Shi, R. X.*, He, T. S.*, Zhu, Y. Y., Wang, T. Y., Lee, M., and Jin, X. M.^# TensorFlow Solver for Quantum PageRank in Large-Scale Networks. Science Bulletin 66, 120-126 (2021).

6.Shi, Z. Y., Tang, H., Feng, Z., Wang, Y., Li, Z. M., Gao, J., Chang, Y. J., Wang, T. Y., Dou, J. P., Zhang, Z. Y., Jiao, Z. Q., Zhou, W. H., and Jin, X, M.^# Quantum Fast Hitting on Glued Trees Mapped on a Photonic chip. Optica 7, 613-618 (2020).

7.Shi, R. X.^#, H. Tang^#, & Jin, X. M.^#  Training a Quantum PointNet with Nesterov Accelerated Gradient Estimation by Projection. Paper No. 8 of the 1st Workshop on Quantum Tensor Networks in Machine Learning at 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

8.Tang, H., Feng, Z., Wang, Y. H., Lai, P. C., Wang, C. Y., Ye, Z. Y., Wang, C. K., Shi, Z. Y., Wang, T. Y., Chen, Y., Gao, J., and Jin, X, M.^# Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks. Physical Review Applied 11, 024020 (2019).

9.Tang, H., Di Franco, C., Shi, Z. Y., He, T. S., Feng, Z., Gao, J., Sun, K., Li, Z. M., Jiao Z. Q., Wang, T. Y., Kim, M. S., and Jin, X. M.^# Experimental quantum fast hitting on hexagonal graphs. Nature Photonics 12, 754-758 (2018).

10.Tang, H., Lin, X. F., Feng, Z., Chen, J. Y., Gao, J., Sun, K., Wang, C. Y., Lai, P. C., Xu, X. Y., Wang, Y., Qiao, L. F., Yang, A. L., and Jin, X. M.^# Experimental Two-dimensional Quantum Walk on a Photonic Chip. Science Advances 4, eaat3174 (2018). 

11.Gao, J., Qiao, L. F., Jiao, Z. Q., Ma, Y. C., Hu, C. Q., Ren, R. J., Yang, A. L., Tang, H., Yung, M. H., and Jin, X. M.^# Experimental Machine Learning of Quantum States. Physical Review Letters 120, 240501 (2018).

See a full list of publications at Google Scholar：https://scholar.google.com/citations?hl=en&user=G7LyfAMAAAAJ

Hao Tang is an active reviewer for journals including: Physical Review Letters, Physical Review Applied, Physical Review Research, Physical Review A, PRX Quantum, IEEE Transactions on Computers, Quantum Science and Technology, Intelligent Computing, APL Photonics.