Machine Learning, artificial intelligence and quantum science at the university of Oslo; research and education
Contents
Basic activities, Overview of our main activities
Quantum Science at CCSE@UiO
More activities
A simple perspective on the interface between ML and Physics
Selected references
Argon-46 by Solli et al., NIMA 1010, 165461 (2021)
Quantum Monte Carlo and deep learning
"Dilute neutron star matter from neural-network quantum states by Fore et al, Physical Review Research 5, 033062 (2023)":"https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.5.033062" at density \( \rho=0.04 \) fm$^{-3}$
The electron gas in three dimensions with \( N=14 \) electrons (Wigner-Seitz radius \( r_s=2 \) a.u.), "Gabriel Pescia, Jane Kim et al. arXiv.2305.07240,":"https://doi.org/10.48550/arXiv.2305.07240"
Quantum Science and Technologies
References, Quantum Computing
Quantum Engineering
Candidate systems
Overview and Motivation
Recent work
Electrons (quantum dots) on superfluid helium
Where we are now
Plans
Courses, theory path
Overview and Motivation
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 (see for example
Sigmundson et al arXiv:2111.09638
)
Time dependence
Optimization of experimental parameters
Feedback from experiment
Finding optimal parameters for tuning of entanglement
Numerical experiments to mimick real systems, using many-body methods to develop
quantum twins
(
inspiration from work by Herschel Rabitz et al on Control of quantum phenomena, see New Journal of Physics 12 (2010) 075008
)!
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