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 and turned to an assistant researcher in March 2020. She is now an associate researcher since December 2020. She 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.

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. Quantum simulation of open quantum systems and biological structures

3. Ising machine and adiabatic quantum computing

4. Quantum computing for machine learning, finance, chemistry and optimization applications

1. 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).

2. 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).

3.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).

4.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).

5.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).

6.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).

7.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).

8.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). 

9.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