arXiv:2601.21310v1 Announce Type: new
Abstract: Stochastic optimization of Neural Network Quantum States (NQS) in discrete Fock spaces is limited by sampling variance and slow mixing. We present a deterministic framework that optimizes a neural backflow ansatz within dynamically adaptive configuration subspaces, corrected by second-order perturbation theory. This approach eliminates Monte Carlo noise and, through a hybrid CPU-GPU implementation, exhibits sub-linear scaling with respect to subspace size. Benchmarks on bond dissociation in H2O and N2, and the strongly correlated chromium dimer Cr2, validate the method’s accuracy and stability in large Hilbert spaces.