Research interests

I am interested in using quantum optimal control and machine learning to advance practical quantum technologies. 

My research is motivated by two central questions: 

 (i) How can one learn and enhance quantum technoloies using artificial intelligence?              

 (ii) How can one optimally manipulates quantum system in the presence of nonlinearities, errors and noises? 

Keywords: Optimal Control; Quantum Information Processing; AI for Quantum Science; Superconducting qubits; Shortcuts to Adiabaticity.

Selected publications

See more publications on arXiv and Google Scholar.

• Huang, T., Du, L.* , & Guo, L.*, Single-Period Floquet Control of Bosonic Codes with Quantum Lattice Gates, Phys. Rev. Lett.137, 060602 (2026) (Continuous-variable Quantum Computing, Bonsonic Codes)

• Huang, T., Gaikwad, A., Moskalenko, et al., Quantum Process Tomography with Digital Twins of Error Matrices, Phys. Rev. Lett. 135, 230601 (2025). (Quantum Computing)

• Huang, T., Yu, Z., Ni, Z., Zhou, X., & Li, X., Quantum force sensing by digital twinning of atomic Bose-Einstein condensates, Communications Physics, 7(1), 172 (2024).(Generative-AI for Quantum Sensing)

• Huang, T., Ban, Y., Sherman, E. Y., & Chen, X., Machine-learning-assisted quantum control in a random environment, Physical Review Applied, 17(2), 024040 (2022). (ML learns Random Noise)

• Huang, T., Malomed, B. A., & Chen, X., Shortcuts to adiabaticity for an interacting Bose–Einstein condensate via exact solutions of the generalized Ermakov equation, Chaos, 30(5) (2020). (Quantum Optimal Control and Shortcuts to adiabaticity)