Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914728097349632 |
|---|---|
| author | Wu, Zheyu Zen, Remmy Casagrande, Heitor P. Bressan, Stéphane Poletti, Dario |
| author_facet | Wu, Zheyu Zen, Remmy Casagrande, Heitor P. Bressan, Stéphane Poletti, Dario |
| contents | Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised training of feed forward neural network quantum states. We empirically and comparatively evaluate the performance of feed forward neural network quantum states in different phases of matter for variants of the architecture, for different hyper-parameters, and for two different loss functions, to which we refer as \emph{mean-squared error} and \emph{overlap}, respectively. We consider the next-nearest neighbor Ising model for the diversity of its phases and focus on its paramagnetic, ferromagnetic, and pair-antiferromagnetic phases. We observe that the overlap loss function allows better training of the model across all phases, provided a rescaling of the neural network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03394 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model Wu, Zheyu Zen, Remmy Casagrande, Heitor P. Bressan, Stéphane Poletti, Dario Statistical Mechanics Quantum Gases Quantum Physics Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised training of feed forward neural network quantum states. We empirically and comparatively evaluate the performance of feed forward neural network quantum states in different phases of matter for variants of the architecture, for different hyper-parameters, and for two different loss functions, to which we refer as \emph{mean-squared error} and \emph{overlap}, respectively. We consider the next-nearest neighbor Ising model for the diversity of its phases and focus on its paramagnetic, ferromagnetic, and pair-antiferromagnetic phases. We observe that the overlap loss function allows better training of the model across all phases, provided a rescaling of the neural network. |
| title | Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model |
| topic | Statistical Mechanics Quantum Gases Quantum Physics |
| url | https://arxiv.org/abs/2305.03394 |