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arXiv Machine Learning By Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei

Direct/adaptive-mixture phase-gradient learning for neural-network quantum states with complex phase structure

Read the original on arXiv Machine Learning →

arXiv:2606. 13912v1 Announce Type: cross Abstract: Neural-network quantum states (NQS) are a leading variational tool for quantum many-body physics, yet their optimization is fragile whenever the ground state carries a non-trivial sign or complex phase structure, a situation generic to gauge fields, broken time-reversal symmetry, and fermionic statistics.

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