Research Interests
Quantum characterization and certification
With the capability of an engineered quantum system to solve computational problems that are intractable otherwise, comes the challenge of verifying that the quantum machine is indeed producing the correct result. The field of quantum characterization, verification, and validation (QCVV) investigates statistical tools and measurement protocols to learn relevant information about quantum states and their transformations.
Selected publications
- N. Dangniam, Y.-G. Han, and H. Zhu, Optimal verification of stabilizer states, arXiv:2007.09713, Physical Review Research 2, 043323 (2020).
- J.A. Gross, N. Dangniam, C. Ferrie, and C.M. Caves, Novelty, efficacy, and significance of weak measurements for quantum tomography, arXiv:1506.08892, Physical Review A 91, 062133 (2015).
Fermionic quantum computation
There exist closed subtheories (sets of states, operations, and measurements) of quantum mechanics that admit an efficient classical simulation, yet are "large" in the sense that an addition of any resource outside of the subtheory enables universal quantum computation. One such subtheory corresponds to the exactly solvable model of free fermions, giving rise to fermionic schemes for quantum computation, the power of which can be characterized using techniques from computational complexity theory and representation theory. (See also my PhD thesis.)
Selected publication
- M. Oszmaniec, N. Dangniam, M.E.S. Morales, and Z. Zimborás, Fermion Sampling: a robust quantum computational advantage scheme using fermionic linear optics and magic input states, arXiv:2012.15825, PRX Quantum 3, 020328 (2022).
Machine learning on quantum computers
Despite the hype surrounding quantum machine learning, combining quantum computing and machine learning in a fruitful way has proved to be far from trivial. Together with researchers at Chula Intelligent and Complex Systems (CHICS), we are interested in rigorously understanding the power and limitation of doing machine learning on quantum computers.
Selected publications
- J. Tangpanitanon, S. Thanasilp, N. Dangniam, M.-A. Lemonde, and D.G. Angelakis, Expressibility and trainability of parametrized analog quantum systems for machine learning applications, arXiv:2005.11222, Physical Review Research 2, 043364 (2020).
- A. Sornsaeng, N. Dangniam, and T. Chotibut, Quantum next generation reservoir computing: an efficient quantum algorithm for forecasting quantum dynamics, arXiv:2308.14239, Quantum Machine Intelligence 6, 57 (2024).
You can find all my publications including preprints on my Google Scholar webpage.