Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

ArXi:2605.13807v1 Announce Type: cross Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by massively parallel architectures such as transformers. Recurrent neural network quantum states, however, are frequently regarded as intrinsically sequential and therefore less scalable. Here we revisit this view by showing that modern recurrent architectures can fast, accurate, and computationally accessible neural quantum state simulations.