Autoregressive Neural Network Extrapolation of Quantum Spin Dynamics Across Time and Space
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915675591671808 |
|---|---|
| author | Pugzlys, Hubert Varude, Shreyas Dillon, Sam Tran, Huy Tang, Ta Jiang, Zhe Ying, Xuzhe Jia, Chunjing |
| author_facet | Pugzlys, Hubert Varude, Shreyas Dillon, Sam Tran, Huy Tang, Ta Jiang, Zhe Ying, Xuzhe Jia, Chunjing |
| contents | Understanding the dynamical response of quantum materials is central to revealing their microscopic properties, yet access to long-time and large-scale dynamics remains severely limited by rapidly growing computational costs and entanglement, particularly in gapless systems. Here we introduce an autoregressive machine-learning framework that enables the extrapolation of dynamical spin correlations in both time and space beyond the reach of conventional numerical methods. Trained on time-dependent density matrix renormalization group simulations of the gapless XXZ model, our approach is benchmarked against exact solutions available for this analytically solvable system. Combined with physics-informed spatial extension, multi-layer perceptron model using ReLU activation functions has been shown to be superior than convolutional neural networks and linear regressions for longer time extrapolation. Perturbation study of error accumulation further demonstrates that our autoregressive neural network extrapolations are highly robust to perturbations, suggesting stable and reliable predictions. This work establishes a new paradigm for studying the dynamics of gapless quantum many-body systems, in which machine learning extends and complements the capabilities of state-of-the-art numerical approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autoregressive Neural Network Extrapolation of Quantum Spin Dynamics Across Time and Space Pugzlys, Hubert Varude, Shreyas Dillon, Sam Tran, Huy Tang, Ta Jiang, Zhe Ying, Xuzhe Jia, Chunjing Strongly Correlated Electrons Disordered Systems and Neural Networks Understanding the dynamical response of quantum materials is central to revealing their microscopic properties, yet access to long-time and large-scale dynamics remains severely limited by rapidly growing computational costs and entanglement, particularly in gapless systems. Here we introduce an autoregressive machine-learning framework that enables the extrapolation of dynamical spin correlations in both time and space beyond the reach of conventional numerical methods. Trained on time-dependent density matrix renormalization group simulations of the gapless XXZ model, our approach is benchmarked against exact solutions available for this analytically solvable system. Combined with physics-informed spatial extension, multi-layer perceptron model using ReLU activation functions has been shown to be superior than convolutional neural networks and linear regressions for longer time extrapolation. Perturbation study of error accumulation further demonstrates that our autoregressive neural network extrapolations are highly robust to perturbations, suggesting stable and reliable predictions. This work establishes a new paradigm for studying the dynamics of gapless quantum many-body systems, in which machine learning extends and complements the capabilities of state-of-the-art numerical approaches. |
| title | Autoregressive Neural Network Extrapolation of Quantum Spin Dynamics Across Time and Space |
| topic | Strongly Correlated Electrons Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2512.13103 |