Thirty Years of Education and Research on Nuclear Many-Body Physics at the ECT*; from traditional Methods to Quantum Computing and Machine Learning
Contents
What is this talk about?
Thanks to many
The first postdocs and long-term visitors, 1994-1996
One of the first many-body workshops, summer 1997
Educational mission and strategic initiatives
The Nuclear TALENT initiative
The first ever Nuclear Talent course, ECT*, summer 2012
The second Nuclear Talent course at the ECT*, summer 2014
The second Nuclear Talent course at the ECT*, summer 2014
Nuclear Talent course summer 2017
Nuclear Talent course summer 2018 in China
Talent courses since 2019, ECT* playing a central role
Educational strategies
And new initiatives
Machine learning. A simple perspective on the interface between ML and Physics
ML in Nuclear Physics
AI/ML and some statements you may have heard (and what do they mean?)
Scientific Machine Learning
Types of machine learning
Main categories
Machine learning and nuclear theory (my bias): Why?
The plethora of machine learning algorithms/methods
Examples of Machine Learning methods and applications in nuclear physics
Examples of Machine Learning methods and applications in nuclear physics, continues
More examples
And more
Selected references
What are the basic ingredients?
Argon-46 by Solli et al., NIMA 1010, 165461 (2021)
Many-body physics, Quantum Monte Carlo and deep learning
Monte Carlo methods and Neural Networks
Deep learning neural networks, "Variational Monte Carlo calculations of \( A\le 4 \) nuclei with an artificial neural-network correlator ansatz by Adams et al.":"https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.022502"
Explicit results
"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"
"Efficient solutions of fermionic systems using artificial neural networks, Nordhagen et al, Frontiers in Physics 11, 2023":"https://doi.org/10.3389/fphy.2023.1061580"
Quantum dots and Boltzmann machines, onebody densities \( N=6 \), \( \hbar\omega=0.1 \) a.u.
Onebody densities \( N=30 \), \( \hbar\omega=1.0 \) a.u.
Onebody densities \( N=30 \), \( \hbar\omega=0.1 \) a.u.
Quantified limits of the nuclear landscape
Constraining the equation of state for dense nuclear matter
Experimental design
Observations and perspectives
How can we use ML in Nuclear Science?
Possible start to raise awareness about ML in our field
Villa Tambosi, summer 1995, Tempus Fugit (sadly)
Quantum computing, Overview and Motivation
What is this about?
Literature and more reading
Quantum Engineering
Candidate systems
Electrons (quantum dots) on superfluid helium
To read more
Experimental setup I
More on experimental setup II
More on experimental setup III
Experimental set up
Entanglement
More on Entanglement
Entanglement gates in trapped ions and more
Quantum dots and the Coulomb interaction
Electrons on helium is another qubit platform
Surface state electrons (SSE)
Calculational details
Calculational details
Calculational details
Calculational details
Calculational details
Calculational details
Calculational details
Calculational details
Calculational details
Results and discussions
Entanglement and more
Legend to figure
Particle densities and coefficients
Potential wells, the one-body densities, and single-particle states
Where we are now
Plans
Addendum: Quantum Monte Carlo Motivation
Quantum Monte Carlo Motivation
Quantum Monte Carlo Motivation
The trial wave function
The correlation part of the wave function
Resulting ansatz
Energy derivatives
Derivatives of the local energy
How do we define our cost function?
Meet the variance and its derivatives
The variance defines the cost function
Potential wells, the one-body densities, and single-particle states
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