From Architectures to Applications: A Review of Neural Quantum States
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909268686405632 |
|---|---|
| author | Lange, Hannah Van de Walle, Anka Abedinnia, Atiye Bohrdt, Annabelle |
| author_facet | Lange, Hannah Van de Walle, Anka Abedinnia, Atiye Bohrdt, Annabelle |
| contents | Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing the state in terms of the network parameters rather than storing all exponentially many coefficients needed for an exact parameterization of the state. We introduce the commonly used NQS architectures and their various applications for the simulation of ground and excited states, finite temperature and open system states as well as NQS approaches to simulate the dynamics of quantum states. Furthermore, we discuss NQS in the context of quantum state tomography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Architectures to Applications: A Review of Neural Quantum States Lange, Hannah Van de Walle, Anka Abedinnia, Atiye Bohrdt, Annabelle Disordered Systems and Neural Networks Quantum Gases Strongly Correlated Electrons Quantum Physics Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing the state in terms of the network parameters rather than storing all exponentially many coefficients needed for an exact parameterization of the state. We introduce the commonly used NQS architectures and their various applications for the simulation of ground and excited states, finite temperature and open system states as well as NQS approaches to simulate the dynamics of quantum states. Furthermore, we discuss NQS in the context of quantum state tomography. |
| title | From Architectures to Applications: A Review of Neural Quantum States |
| topic | Disordered Systems and Neural Networks Quantum Gases Strongly Correlated Electrons Quantum Physics |
| url | https://arxiv.org/abs/2402.09402 |