arXiv · 2610.40022
Learning nuclear mass systematics with hybrid quantum-classical mixtures of experts
Abstract
We introduce a mixture-of-experts framework combining sixteen hybrid quantum-classical neural networks with nucleus-dependent classical gating to learn residual corrections to the WS4 nuclear mass model. On a fixed AME2020 test set, the framework reduces the root-mean-square error from 456.22 to 179.83 keV and outperforms a parameter-matched classical counterpart. The learned corrections improve shell-gap systematics near the Sn and Pb shell closures, while analysis of the gating networks identifies shell-related descriptors as the leading inputs. For 66Ca, the model predicts Sn = 0.577 MeV and S2n = -0.252 MeV, allowing two-neutron emission while the one-neutron channel remains closed.
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Shijiang Ji, Heyi Yang, Zu-Xing Yang, Xiao-Hua Fan, Zhi-Pan Li, Haozhao Liang. 2026-10-29. Learning nuclear mass systematics with hybrid quantum-classical mixtures of experts. https://arxiv.org/abs/2610.40022
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