
Ninnat Dangniam, Ph.D.
- Lecturer
- Head of the QIS Lab
Laboratory: Laboratory of Quantum Information Science (QIS)
Office: TA 207.1
Email: ninnatd@nu.ac.th
Blogs/Pages: One Quantum at a Time (inactive), Schur's Lemming, Schrödinger's Capy
- Ph.D. in Physics, Center for Quantum Information and Control (CQuIC), University of New Mexico
- B.Sc. in Physics and B.Sc. in Mathematics, summa cum laude, University of Oregon
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
- A. Sornsaeng, N. Dangniam, P. Palittapongarnpim and T. Chotibut, Quantum diffusion map for nonlinear dimensionality reduction, arXiv:2106.07302, Physical Review A 104, 052410 (2021).
- 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).
You can find all my publications including preprints on my Google Scholar webpage.
Book chapter
- ขั้นคิดคำนวณแบบควอนตัม ใน รูป รส กลิ่น เสียง สัมผัส ไอทีควอนตัม (๓): “คอมพิวเตอร์เชิงควอนตัม” (ความรู้รอบตัว พ.ศ. ๒๕๖๐), เกียรติศักดิ์ ศรีพิมานวัฒน์ (บรรณาธิการ), OQC Academy [บทความเต็ม+ส่วนขยาย]
Workshops/Short Courses
- 2023 IF Summer School [lecture notes]
- Quantum Computing คณิตกรณ์ควอนตัม, 4-6 March 2023
- 2022 NAS-IF Summer School for Young Physicists on Cosmology, Quantum Information, and Quantum Field Theory [course github]
- Co-teaching IF Winter School Quantum Information ก้าวแรกสู่นักควอนตัมเทคโนโลยี, 30 October - 1 November 2021 [lecture notes]
Selected invited talks
- Quantum Sampling Advantage: How to prove it and does it matter?, ANSCSE 26 (20 July 2023)
- Fermion Sampling: Robust quantum computational advantage scheme using fermionic linear optics and magic input states, Quantum Technology Theory Group seminar, Department of Materials, University of Oxford (24 May 2022)
- Doing quantum computing researches as theoretical physicists, IF x QTFT MOU signing event (25 February 2022)
- Quantum advantage: classical hardness in correlation game and random sampling, โครงการบรรยายวิชาการเรื่อง เทคโนโลยีควอนตัมคอมพิวเตอร์, Mahasarakham University (26 January 2022)
- Robust quantum computational advantage scheme using fermionic linear optics and magic input states, David Gosset's internal group meeting, IQC, University of Waterloo (18 March 2021),
- Optimal verification of stabilizer states, Centre for Quantum Dynamics seminar, Griffiths University (1 November 2019)
- Quantum supremacy on near-term quantum computers, QTFT public webinar (7 September 2019)
- Quasi-probabilities on fermionic phase spaces, IF Colloquium (7 August 2018)
- Teaching M897520 Non-Relativistic Quantum Mechanics (June - October 2022) [course github]
- Co-teaching M897513 Equilibrium Statistical Mechanics and Kinetic Theory (November 2022 - March 2023)
- Co-teaching M897564 Non-Relativistic Quantum Mechanics (June - October 2022)
- Co-teaching M897562 Classical Electrodynamics (November 2021 - March 2022)
Master of Science (MSc) Student
- Matachan Oupatam (DPST Scholar)