Speaker
Description
Solving the quantum many-body problem entails nontrivial difficulties stemming from the exponential growth of the Hilbert-space dimension. Artificial neural networks have proven to be flexible tools for compactly representing quantum many-body states. I will present a variational Monte Carlo method based on neural-network quantum states that solves the nuclear Schrödinger equation in a systematically improvable fashion, with polynomial scaling in the number of nucleons. This method enables quantum Monte Carlo calculations of medium-mass nuclei, allowing us to study the essential elements of nuclear binding, e. g. the simplest nuclear Hamiltonian capable of describing binding energies and charge radii across the nuclear chart with few-percent accuracy. I will then present applications to condensed-matter systems, such as the ultra-cold Fermi gases in the unitary limit. Perspectives on accessing electroweak responses and the real-time dynamics of quantum many-body systems will also be discussed.
| Theoretical or experimental | Theoretical |
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