Unsupervised Techniques to Detect Quantum Chaos
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909692570107904 |
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| author | Nemirovsky, Dmitry Shir, Ruth Rosa, Dario Kagalovsky, Victor |
| author_facet | Nemirovsky, Dmitry Shir, Ruth Rosa, Dario Kagalovsky, Victor |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Techniques to Detect Quantum Chaos Nemirovsky, Dmitry Shir, Ruth Rosa, Dario Kagalovsky, Victor Quantum Physics Chaotic Dynamics 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. |
| title | Unsupervised Techniques to Detect Quantum Chaos |
| topic | Quantum Physics Chaotic Dynamics |
| url | https://arxiv.org/abs/2507.12887 |