Quantum computing, Machine Learning and Quantum Machine Learning at UiO
Center for Computing in Science Education and Computational Physics Research Group, Department of Physics, UiO
Jan 3, 2024
People
UiO
- Morten Hjorth-Jensen (theory), Lasse Vines, Marianne Bathen Etzelmueller, Justin Wells and David Gongarra (experiment)
- Four theory PhD students (2019-2025), one PD shared with Lasse Vines' QuTE project.
- Nine master of science students (theory), many-body physics, quantum computing, quantum machine learning and machine learning
MSU
- Dean Lee, Scott Bogner, Angela Wilson and Heiko Hergert, theory and Johannes Pollanen and Niyaz Beysengulov, experiment
- Four PhD students working on quantum computing and machine learning (theory)
Since 2020, final thesis of three PhD students (MSU) and ten master of science students (UiO).
Educational strategies
- New study direction on Quantum technology in Bachelor program Physis and Astronomy, starts Fall 2024. Three new courses:
- FYS1400 Introduction to Quantum Technologies
- FYS3405/4405 Quantum Materials
- FYS3415/4415 Quantum Computing
- Developed Master of Science program on Computational Science, started fall 2018 and many students here work on quantum computing and machine learning
- Developed courses on machine learning, from basic to advanced ones, FYS-STK3155/4155 and FYS5429/9429
- Developed advanced course on quantum computing and quantum machine learning, FYS5419/9419
- Since 2019 organized and taught more than twenty international schools and intensive courses on quantum computing and machine learning
More on educational research and strategies
- Member of the National Science Foundation sponsored project QSTEAM, see https://qusteam.org/.
- Here we have over three years developed educational material for several basic courses in quantum technology, quantum computing and quantum information theory.
- This is a collaboration between several universities in the midwest and involves close to 60 university faculty
The courses are aimed at undergraduate students in the STEM fields.
Machine learning research
- Solving complicated quantum mechanical many-body systems with deep learning, see references at the end
- Developing new machine learning algorithms with applications to quantum computing as well
- Analyzing experimental data from nuclear physics experiments, NIMA https://www.sciencedirect.com/science/article/abs/pii/S0168900221004460?via%3Dihub
- Predicting solid state material platforms for quantum technologies, Nature Computational Materials https://www.nature.com/articles/s41524-022-00888-3
Quantum computing and quantum machine learning, main activities
How to use many-body theory to design quantum circuits (Quantum engineering)
- Many-body methods like F(ull)C(onfiguration)I(nteraction) theory with
- Adaptive basis sets
- Time dependence
- Optimization of experimental parameters
- Feedback from experiment
- Finding optimal parameters for tuning of entanglement
- Numerical experiments to mimick real systems, quantum twins
- Constructing quantum circuits to simulate specific systems
- Quantum machine learning to optimize quantum circuits
Candidate systems at UiO and MSU
- Quantum dots, experiments at MSU and UiO
- Point Defects in semiconductors, experiments at UiO
- Recent article Coulomb interaction-driven entanglement of electrons on helium, see https://arxiv.org/abs/2310.04927, and submitted to Physical Review X Quantum
Selected references
- Artificial Intelligence and Machine Learning in Nuclear Physics, Amber Boehnlein et al., Reviews Modern of Physics 94, 031003 (2022)
- Dilute neutron star matter from neural-network quantum states by Fore et al, Physical Review Research 5, 033062 (2023)
- Neural-network quantum states for ultra-cold Fermi gases, Jane Kim et al, Nature Physics Communication, in press
- Message-Passing Neural Quantum States for the Homogeneous Electron Gas, Gabriel Pescia, Jane Kim et al. arXiv.2305.07240,
- Efficient solutions of fermionic systems using artificial neural networks, Nordhagen et al, Frontiers in Physics 11, 2023
More selected references
Machine Learning and Quantum Computing grants
- 2021-2025 9M NOK from RCN, Norway, QUantum emitters in semiconductors for future TEchnologies, co-PI
- 2023-2025 1M USD from Department of Energy, USA, STREAMLINE Collaboration: Machine Learning for Nuclear Many-Body Systems, co-PI.
- 2023-2026 450 kUSD from Department of Energy, USA, Effective Field Theory and Renormalization Group Studies of Quantum Algorithms, co-PI.
- 2020-2023 750 kUSD from the Department of Energy, USA, From Quarks to Stars; A Quantum Computing Approach to the Nuclear Many-Body Problem. PI.