Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.01996 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916208633184256 |
|---|---|
| author | Chen, Zhuo Newhouse, Laker Chen, Eddie Luo, Di Soljačić, Marin |
| author_facet | Chen, Zhuo Newhouse, Laker Chen, Eddie Luo, Di Soljačić, Marin |
| contents | Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While representing quantum states with tensor networks and neural networks are the two state-of-the-art methods for approximate simulations, each has its own limitations in terms of expressivity and inductive bias. To address these challenges, we develop a novel architecture, Autoregressive Neural TensorNet (ANTN), which bridges tensor networks and autoregressive neural networks. We show that Autoregressive Neural TensorNet parameterizes normalized wavefunctions, allows for exact sampling, generalizes the expressivity of tensor networks and autoregressive neural networks, and inherits a variety of symmetries from autoregressive neural networks. We demonstrate our approach on quantum state learning as well as finding the ground state of the challenging 2D $J_1$-$J_2$ Heisenberg model with different systems sizes and coupling parameters, outperforming both tensor networks and autoregressive neural networks. Our work opens up new opportunities for quantum many-body physics simulation, quantum technology design, and generative modeling in artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01996 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation Chen, Zhuo Newhouse, Laker Chen, Eddie Luo, Di Soljačić, Marin Quantum Physics Strongly Correlated Electrons Machine Learning Computational Physics Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While representing quantum states with tensor networks and neural networks are the two state-of-the-art methods for approximate simulations, each has its own limitations in terms of expressivity and inductive bias. To address these challenges, we develop a novel architecture, Autoregressive Neural TensorNet (ANTN), which bridges tensor networks and autoregressive neural networks. We show that Autoregressive Neural TensorNet parameterizes normalized wavefunctions, allows for exact sampling, generalizes the expressivity of tensor networks and autoregressive neural networks, and inherits a variety of symmetries from autoregressive neural networks. We demonstrate our approach on quantum state learning as well as finding the ground state of the challenging 2D $J_1$-$J_2$ Heisenberg model with different systems sizes and coupling parameters, outperforming both tensor networks and autoregressive neural networks. Our work opens up new opportunities for quantum many-body physics simulation, quantum technology design, and generative modeling in artificial intelligence. |
| title | ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation |
| topic | Quantum Physics Strongly Correlated Electrons Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2304.01996 |