| Full text | |
| Author(s): |
Nemirovsky, Dmitry
;
Shir, Ruth
;
Rosa, Dario
;
Kagalovsky, Victor
Total Authors: 4
|
| Document type: | Journal article |
| Source: | LOW TEMPERATURE PHYSICS; v. 50, n. 12, p. 8-pg., 2024-12-01. |
| Abstract | |
Conventional spectral probes of quantum chaos require eigenvalues, and sometimes, eigenvectors of the quantum Hamiltonian. This involves computationally expensive diagonalization procedures. We test whether an unsupervised neural network can detect quantum chaos directly from the Hamiltonian matrix. We use a single-body Hamiltonian with an underlying random graph structure and random coupling constants, with a parameter that determines the randomness of the graph. The spectral analysis shows that increasing the amount of randomness in the underlying graph results in a transition from integrable spectral statistics to chaotic ones. We show that the same transition can be detected via unsupervised neural networks, or more specifically, self-organizing maps by feeding the Hamiltonian matrix directly into the neural network, without any diagonalization procedure. (AU) | |
| FAPESP's process: | 21/14335-0 - ICTP South American Institute for Fundamental Research: a regional center for Theoretical Physics |
| Grantee: | Nathan Jacob Berkovits |
| Support Opportunities: | Special Projects |
| FAPESP's process: | 23/11832-9 - Quantum many-body systems: from quantum chaos to quantum technologies |
| Grantee: | Dario Rosa |
| Support Opportunities: | Research Grants - Young Investigators Grants |