Research topics
Quantum Machine Learning and Algorithms, Statistical Mechanics and Complex Systems
Contact: thiparat.c [AT] chula.ac.th
Office: MHMK Building Room 1905
Education & past employments
Faculty member, Department of Physics, Faculty of Science, Chulalongkorn University (2019-present)
Co-founder and scientific advisor of Quantum Technology Foundation (Thailand) (QTFT) (2020-present)
Visiting scholar at EPFL, Switzerland (2025, 2023), NTU, Singapore (2025, 2024) & KITP, Santa Barbara, USA (2019)
Postdoctoral scholar, SUTD, Singapore
Ph.D. & M.A. in Theoretical Physics, Harvard University, USA
M.A. in Mathematics & B.S. in Physics (Highest Distinction, Phi Beta Kappa), The University of Virginia, USA
About me: I’m an assistant professor of physics working at the intersection of physics and computer science. Our current research aims to develop a fundamental understanding of learning algorithms and information processing systems that operate across diverse physical substrates, drawing insights from computational neuroscience, statistical physics, and quantum information science. I also work closely with industry partners, solving real-world problems using tools from network science and optimization theory.
Outreach and academic services
Program committee: Quantum Techniques in Machine Learning (QTML2023, 2024, 2025),
Organizing committee: IEEE Globecom 2025 workshop on Quantum Computing for Communications and Learning (link),
Bangkok Workshops on Discrete Geometry, Dynamics & Statistics 2022-2026 (link), Siam Quantum Science and Technology International Conference 2026 (SQST2026)Reviewer: Physical Review Journals, Nature Portfolios, IOP Journals, Neurocomputing, AAAI, NeurIPS.
Media Partners: Suthichai Live (link), the Standard (link), Tam-Eig (link)
Quantum Ecosystem and Education by Techsauce Ep. [1][2][3][4][5]
BrainCodeCamp: AI and computational neuroscience for enthusiastic Thais (link)
Selected talks
Highlighting current research themes in information processing across physical systems:
What can Quantum AI learn from biological intelligence? [link][slides]
Biological, Artificial, Quantum Intelligence (BAQ 2026), Okinawa Institute of Science and Technology, Japan (March 2026)Robust working memory in noisy neuronal networks [video][slides]
Computing with Physical Systems, Les Houches, France (January 2026),
Marian Smoluchowski symposium on statistical physics, Mark Kac Complex Systems Research Center, Poland
(September 2025) [link]On fundamental aspects of quantum extreme learning machines and reservoir computing [video][slides]
Quantum Techniques in Machine Learning Conference 2024, Melbourne, Australia (November 2024)From explainable NLP to quantum dynamics prediction [video][slides]
QAISG Seminar, Centre for Quantum Technologies, NUS, Singapore (February 2024)
Selected publications
Srimahajariyavong, K., Thanasilp, S., Chotibut, T.Connecting phases of matter to the flatness of loss landscape in analog variational quantum algorithms. Communications Physics (In press 2026) [link][news][Kasidit’s talk]
Rungratsameetaweemana, N., Kim, R., Chotibut, T., Sejnowski, T. Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation. Proc. Natl. Acad. Sci. U.S.A. 122 (3) e2316745122 (2025) [link][news]
Bielawski, J., Chotibut, T., Falniowski, F., Misiurewicz, M., and Piliouras, G. Heterogeneity, reinforcement learning and chaos in population games. Proc. Natl. Acad. Sci. U.S.A 122 (25) e2319929121 (2025) [link]
Xiong, W., Facelli, G., Sahebi, M., Agnel, O., Chotibut, T., Thanasilp, S., and Holmes, Z. On fundamental aspects of quantum extreme learning machines. Quantum Mach. Intell. 7, 20 (2025) [link] [talk]
Sornsaeng, A., Dangniam, N., and Chotibut, T. Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics. Quantum Mach. Intell. 6, 57 (2024) [link] [slides from talk]
Tangpanitanon, J., Saiphet, J.,…, Chotibut, T.Hybrid Quantum-Classical Algorithms for Loan-Collection Optimization with Loan-Loss Provisions. Phys. Rev. Applied 19, 064001 (2023) [industry collaboration with QTFT and KBTG] [link]
Pakornchote, T., Ektarawong, A., Chotibut, T. StrainTensorNet:Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks. Phys. Rev. Res. 5 (4), 043198 (2023) [link]
Tangpanitanon, J., Mangkang, C., Bhadola, P., Minato, Y., Angelakis, D., Chotibut, T.Explainable Natural Language Processing with Matrix Product States. New J. Phys. 24 053032 (2022) [featured on tensornetwork.org] [link][talk]
Sornsaeng, A., Dangniam, N., Palittapongarnpim, P., Chotibut, T. Quantum diffusion map for nonlinear dimensionality reduction. Phys. Rev. A 104, 052410 (2021) [link]
Chotibut, T., Falniowski, F., Misiurewicz, M., Piliouras, G. The route to chaos in routing games: When is Price of Anarchy too optimistic? Advances in Neural Information Processing Systems 33 - NeurIPS 2020 [link]
Chotibut, T., Succi, S., and Nelson, D. R. Striated populations in disordered environments with advection. Physica A, 465, 500-514 (2017) [link]
Chotibut, T., Nelson, D. R. Population genetics with fluctuating population sizes. Journal of Statistical Physics: special edition dedicated to the memory of Leo Kadanoff, 167 (3-4) 777-791 (2017) [link]
Papers under review
Chotibut, T., Evnin, O., and Horinouchi, W. Random matrix theory of sparse neuronal networks with heterogeneous timescales.
[link] [arXiv:2512.12767]Xiong, W., Holmes, Z., Angrisani, A., Suzuki, Y., Chotibut, T., and Thanasilp, S. Role of scrambling and noise in temporal information processing with quantum systems. [link] [arXiv:2505.10080]
Mhiri, H., Puig, R., Lerch, S., Rudolph, M., Chotibut, T., Thanasilp, S., and Holmes, Z. A unifying account of warm start guarantees for patches of quantum landscapes. [link] [arXiv:2502.07889]
Kumpeerakij, C., Chotibut, T., and Kogan, O. Aging in a two-dimensional swarmalators crystal with delayed interactions.
[link] [arXiv:2508.07429]Tangsongcharoen, K., Pakornchote, T., …, and Chotibut, T. CrystalGRW: generative modeling of crystal structures with targeted properties via geodesic random walks. [link] [arXiv:2501.08998]
Research News
Job opportunities
I am looking for driven and motivated postdocs and students. If you're interested in joining our team, please send your CV and a two-page research statement to thiparat.c[at]chula.ac.th.
