Publication

Preprint

Z. He*, D. Amaro*, R. Shaydulin, M. Pistoia, “Performance of quantum approximate optimization with quantum error detection,” 2024 (* Equal contributions) [arXiv]
T. Hao*, Z. He*, R. Shaydulin, J. Larson, M. Pistoia, “End-to-end protocol for high-quality QAOA parameters with few shots,” 2024 (* Equal contributions) [arXiv]
Z. Liang, Z. He, Y. Sun, D. Herman, Q. Jiao, Y. Zhu, W. Jiang, X. Xu, D. Wu, M. Pistoia, Y. Shi, "Synergizing quantum techniques with machine learning: the drug discovery challenge,’’ submitted to Scientific Reports.
F. Liu*, Z. He*, Kang Hao Cheong, "When long tail meets adversarial robustness: A re-balanced divergence-enhanced adversarial training,’’ submitted to IEEE Trans on Neural Networks and Learning Systems (TNNLS). (* Equal contributions)

Z. He, R. Shaydulin, D. Herman, C. Li, R. Raymond, S. Sureshbabu, M. Pistoia, “Parameter setting heuristics make the quantum approximate optimization algorithm suitable for the early fault-tolerant era,” International Conf. Computer Aided Design (ICCAD), Newark, NJ, Oct. 2024. (Invited paper) [arXiv]
T. Hao, Z. He, R. Shaydulin, M. Pistoia, S. Tannu, "Variational quantum algorithm landscape reconstruction by low-rank tensor completion,’’ IEEE International Conference on Quantum Computing and Engineering (QCE) 2024. [arXiv]
Z. He, S. Chakrabarti, D. Herman, N. Kumar, C. Li, P. Minseen, P. Niroula, R. Shaydulin, Y. Sun, S. Sureshbabu, R. Yalovtzky, M. Pistoia, "Challenges and opportunities of quantum optimization in finance,’’ ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jun. 2024. (Invited paper)
Z. He, B. Peng, Y. Alexeev, Z. Zhang, "Distributionally robust variational quantum algorithms with shifted noise,’’ IEEE Trans on Quantum Engineering, vol. 5, pp. 1-12, 2024. [arXiv]
Z. Gao, Z. Zhang, Z. He, J. Gu, D. Pan, D. Boning, "Selecting robust silicon photonic designs after Bayesian optimization without extra simulations,’’ accepted by Optics Express.

Z. He, "Uncertainty-aware and efficient design automation from classical circuit to quantum computing,’’ PhD thesis.
Z. He, R. Shaydulin, S. Chakrabarti, D. Herman, C. Li, Y. Sun, M. Pistoia, "Alignment between initial state and mixer improves QAOA performance for constrained optimization,’’ npj Quantum Information, 9, 121 (2023). [journal link] [arXiv] [Highlighed in Quantinuum's Press release] – Presented in APS March Meeting 2024 [link]
R. Solgi, Z. He, W. J. Liang, Z. Zhang, and H. Loaiciga "Tensor shape search for efficient compression of tensorized data and neural networks,’’ Applied Soft Computing, 149, 110987, 2023. [link] [short version in arXiv]
Y. Pan*, Z. He*, N. Guo, Z. Zhang, "Distributionally robust circuit design optimization under variation shifts,’’ International Conf. Computer Aided Design (ICCAD), San Francisco, CA, Oct. 2023. (* Equal contributions) [arXiv][slides]
Z. Liang, Z. Song, J. Cheng, Z. He, J. Liu, H. Wang, R. Qin, Y. Wang, S. Han, X. Qian, and Y. Shi, "Hybrid gate-pulse model for variational quantum algorithms,’’ ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 2023. [arXiv]

S. Gulania*, Z. He* , B. Peng*, N. Govind and Y. Alexeev, "QuYBE - An algebraic compiler for quantum circuit compression,’’ ACM/IEEE Symposium on Edge Computing (SEC) Workshop on Quantum Computing. (* Equal contributions) [arXiv][Github] – Presented in APS March Meeting 2023 [link]
C. Ibrahim, D. Lykov, Z. He, Y. Alexeev, I. Safro, ‘‘Constructing optimal contraction trees for tensor network quantum circuit simulation,’’ IEEE High Performance Extreme Computing Conference (HPEC), 2022. (Best Student Papar award) [arXiv]
Z. He, B. Zhao and Z. Zhang, "Active sampling for accelerated MRI with low-rank tensors,’’ In Proceedings of the Annual International Conference on the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. (Oral) [arXiv][slides]

Z. He and Z. Zhang, "PoBO: A polynomial bounding method for chance-constrained yield-aware optimization of photonic ICs,’’ IEEE Trans. Computer-Aided Design for Integrated Circuits and Systems (TCAD), vol. 41, no. 11, pp. 4915-4926, Nov. 2022. [arXiv][poster] [video]
Z. He and Z. Zhang, "High-dimensional uncertainty quantification via tensor regression with rank determination and adaptive sampling,’’ IEEE Trans. Components, Packaging and Manufacturing Technology (TCPMT), vol. 11, no. 9, pp. 1317-1328, Sept. 2021. (Ivited paper) [arXiv]
Z. He and Z. Zhang, "Progress of tensor-based high-dimensional uncertainty quantification of process variations,’’ International Applied Computational Electromagnetics Society (ACES) Symposium, virtually, Aug. 2021. (Invited paper) [slides][video]

Z. He and Z. Zhang, "High-dimensional uncertainty quantification via active and rank-adaptive tensor regression,’’ IEEE Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, CA, Oct. 2020. (Best Student Papar award) [arXiv][slides][video]

Z. He, W. Cui, C. Cui, T. Sherwood and Z. Zhang, "Efficient uncertainty modeling for system design via mixed integer programming,’’ International Conf. Computer Aided Design (ICCAD), Westminster, CO, Nov. 2019. (Acceptance rate = 23.8%) [arXiv][slides]

Z. He, F.T.S. Chan and W. Jiang, "A quantum framework for modelling subjectivity in multi-attribute group decision making,’’ Computers & Industrial Engineering, 124 (2018): 560-572.
Z. He and W. Jiang, "An evidential Markov decision making model,’’ Information Sciences, 467 (2018): 357-372.
Z. He and W. Jiang, "An evidential dynamical model to explain the interference effects of categorization on decision making results,’’ Knowledge-Based Systems, 150 (2018): 139-149.
Z. He and W. Jiang, "A new belief Markov chain model and its application in inventory prediction,’’ International Journal of Production Research, 56 (2018), 2800-2817.
Z. He, W. Jiang and F.T.S. Chan., "Evidential supplier selection based on interval data fusion,’’ International Journal of Fuzzy Systems, 20 (2018): 1159-1171.

W. Jiang, Y. Cao, L. Yang and Z. He, "A Time-Space domain information fusion method for specific emitter identification based on Dempster-Shafer evidence theory,’’ Sensors, 17(9) (2017): 1972.
Y. Tang, D. Zhou, Z. He and S. Xu, "An improved belief entropy-based uncertainty management approach for sensor data fusion,’’ International Journal of Distributed Sensor Networks, 137 (2017): 1550147717718497.
Z. He and W. Jiang, "Quantum mechanical approach to modelling reliability of sensor reports,’’ IEEE Sensors Letters, 1 (2017): 1-4.
Y. Tang, D. Zhou, S. Xu and Z. He, "A weighted belief entropy-based uncertainty measure for multi-sensor data fusion,’’ Sensors, 17(4) (2017): 928.