Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States
Neural Quantum States based on autoregressive recurrent neural network (RNN) wave functions enable efficient sampling without Markov-chain autocorrelation, but standard RNN architectures are biased toward finite-length correlations and can fail on states with long-range dependencies. A common response is to adopt transformer-style self-attention, but this typically comes with substantially higher computational and memory overhead. Here we introduce dilated RNN wave functions, where recurrent units access distant sites through dilated connections, injecting an explicit long-range inductive bias while retaining a favorable $\mathcal{O}(N \log N)$ forward pass scaling. We show analytically t...
| Authors | Asif Bin Ayub, Amine Mohamed Aboussalah, Mohamed Hibat-Allah |
|---|---|
| Primary category | quant-ph |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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