Dr. TANG-YOU HUANG (黄堂友)
Dr. TANG-YOU HUANG (黄堂友)
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Biography
My name is Tang-You (堂友) Huang (黄), a postdoctoral researcher at Chalmers University of Technology, focusing on quantum science and technology with quantum optimal control and machine learning, across both theory and experiment. I completed my Ph.D. at Shanghai University in 2022, where I studied quantum optimal control and shortcuts to adiabaticity for cold atoms from a theoretical perspective. I later worked shortly at the Shanghai Qizhi Institute, collaborating with experimental groups to implement machine-learning–based quantum technologies. Since 2023, I have been a postdoctoral researcher within WACQT, a national program to build a large-scale quantum computer in Sweden. Currently, I am interested in discrete- (continous-) variable-based quantum information/computing applications with quantum control and machine-learning technique, particularly, embed in a superconducting platform. Outside of academia, I have a passion for outdoor activities, particularly hiking and skiing.
Research interests
Keywords: Machine Learning; Quantum Optimal Control; Quantum Information Processing; Superconducting qubits;Shortcuts to Adiabaticity; Variational Quantum Algorithms;
See more details on Research.
Quantum Information/Computing with Optimal Control
Quantum error correction (QEC) [Ref] provides a practical path to fault-tolerant quantum computation relied on discrete-variable (DV) and continuous-variable (CV) [Ref] , requiring precise quantum control and efficient information processing. Along this line, we have explored superconducting quantum gate (discrete-variable) optimization [Ref], bosonic code qubits (continuous-variable) [Ref], and quantum algorithm implementation [Ref1, Ref2].
AI/ML for Quantum Technologies
AI/ML for Quantum Technologies
We all know that the quantum features and resources are valuable but fragile in the presence of noise and errors. How can we efficiently learn the statistical behavior and further offer a better engineering protocol of quantum technologies under imperfection? Machine learning (ML) provides a powerful tool for this question [Ref]. In past years, we developed ML-based quantum control in a random environment [Ref], generative-AI-enhanced quantum sensing [Ref] and quantum process tomography [Ref].
Selected publications
Huang, T., Gaikwad, A., Moskalenko, et al., Quantum Process Tomography with Digital Twins of Error Matrices, Phys. Rev. Lett. 135, 230601 (2025); arXiv:2505.07725 (2025). (AI for Quantum Technology)
Guo, L., Huang, T.*, & Du, L.*, Engineering Fault-tolerant Bosonic Codes with Quantum Lattice Gates, arXiv preprint arXiv:2410.17069 (2024). Communications Physics, 8, 414(2025). (Continuous-variable Quantum Computing)
Huang, T.-Y., Malomed, B. A., & Chen, X., Shortcuts to adiabaticity for an interacting Bose–Einstein condensate via exact solutions of the generalized Ermakov equation, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5) (2020). (Quantum Optimal Control)
See the full list of publications on arXiv and Google Scholar.
Teaching & Supervising
2025-2026, Master thesis supervision "Efficient Optimal Control of Superconducting Quantum Gates" at MC2, Chalmers University of Technology.
2025-June, Master course "Open Quantum System" at MC2, Chalmers University of Technology.
As a lab assistant to guide master students in experimentally learning superconducting qubit characterization.
Oral presentation
(Nov 30, 2025 ) invited talk (online), Shanghai University
Talk Title: "Generative AI for Quantum Technologies"
(July 7-13, 2025) Contributed talk, ICMM-CSIC, Madrid
The 9th International Conference on "Quantum Information, Spacetime Simulation, and Quantum Topological Matter (QuIST IX)"
Talk Title: "Improved Quantum Process Tomography"
(2 Jan - 5 Jan, 2024) Visiting talk, Tianjin University , China
I was invited to Tianjin University by Prof. LingZhen Guo at the Center for Joint Quantum Studies.
Talk Title: "Quantum force sensing by digital twinning of atomic Bose-Einstein condensates."
Feel free to contact me by Email: tangyou@chalmers.se.
Useful links: Orcid: ➡️ Google Scholar ➡️ Github Homepage: ➡️ arXiv: ➡️
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