Thanasilp Supanut
Former CQT PhD StudentStatus: Alumnus (data might not be up-to-date)
Preprints
- Yong-Guang Zheng, Wei-Yong Zhang, Ying-Chao Shen, An Luo, Ying Liu, Ming-Gen He, Hao-Ran Zhang, Wan Lin, Han-Yi Wang, Zi-Hang Zhu, Ming-Cheng Chen, Chao-Yang Lu, S. Thanasilp, D.G. Angelakis, Zhen-Sheng Yuan, Jian-Wei Pan. Efficiently Extracting Multi-Point Correlations of a Floquet Thermalized System.
- S. Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, M. Cerezo. Subtleties in the trainability of quantum machine learning models.
- J. Jirawat Tangpanitanon, J. Tangpanitanon, S. Thanasilp, Marc-Antoine Lemonde, D.G. Angelakis. Quantum supremacy with analog quantum processors for material science and machine learning.
Publications
- J. Jirawat Tangpanitanon, J. Tangpanitanon, S. Thanasilp, Marc-Antoine Lemonde, Ninnat Dangniam, D.G. Angelakis. (2023). Signatures of a sampling quantum advantage in driven quantum many-body systems. Quantum Science and Technology 8 025019
- S. Thanasilp, J. Jirawat Tangpanitanon, J. Tangpanitanon, Marc-Antoine Lemonde, Ninnat Dangniam, D.G. Angelakis. (2021). Quantum supremacy and quantum phase transitions. Phys. Rev. B 103 165132
- Benjamin Tan, Marc-Antoine Lemonde, S. Thanasilp, J. Jirawat Tangpanitanon, J. Tangpanitanon, D.G. Angelakis. (2021). Qubit-efficient encoding schemes for binary optimisation problems. Quantum 5 454
- J. Jirawat Tangpanitanon, J. Tangpanitanon, S. Thanasilp, Ninnat Dangniam, Marc-Antoine Lemonde, D.G. Angelakis. (2020). Expressibility and trainability of parameterized analog quantum systems for machine learning applications. Physical Review Research 2 043364
