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Nuclear responses with neural-network quantum states
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abstract
We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. We demonstrate that a simple nuclear Hamiltonian, based on a leading-order pionless effective field theory expansion and known to accurately reproduce the ground-state energies of nuclei with $A\leq 20$ nucleons also provides a reliable description of the photoabsorption cross section.
Forward citations
Cited by 3 Pith papers
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Medium-mass nuclei with neural quantum states
Pfaffian-Jastrow neural quantum states yield ground-state energies and charge radii for nuclei up to A=58, with weak p-wave terms reducing average energy error to ~3% while revealing Hamiltonian sensitivity and A^3 scaling.
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For the doped square-lattice Hubbard model, hidden-fermion and backflow neural quantum states with a Vision Transformer backbone reach nearly equal variational energies; translation-equivariant attention is outperform...
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Studying few cluster resonances with quantum neural network driven iterative Harrow-Hassidim-Lloyd algorithm
A quantum neural network generates eigenvector-continuation basis states, and an iterative HHL routine solves the resulting generalized eigenvalue problem for the 4+ resonance of 9ΛBe.
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