Holographic generative flows with AdS/CFT
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915761330585600 |
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| author | Mirafzali, Ehsan Shashi, Sanjit Murdeshwar, Sanya Shaghoulian, Edgar Venturi, Daniele Marinescu, Razvan |
| author_facet | Mirafzali, Ehsan Shashi, Sanjit Murdeshwar, Sanya Shaghoulian, Edgar Venturi, Daniele Marinescu, Razvan |
| contents | We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, with techniques for deep learning and transport theory. Our proposal is to represent the flow of data from a base distribution to some learned distribution using the bulk-to-boundary mapping of scalar fields in AdS. In the language of machine learning, we are representing and augmenting the flow-matching algorithm with AdS physics. Using a checkerboard toy dataset and MNIST, we find that our model achieves faster and higher quality convergence than comparable physics-free flow-matching models. Our method provides a physically interpretable version of flow matching. More broadly, it establishes the utility of AdS physics and geometry in the development of novel paradigms in generative modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22033 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Holographic generative flows with AdS/CFT Mirafzali, Ehsan Shashi, Sanjit Murdeshwar, Sanya Shaghoulian, Edgar Venturi, Daniele Marinescu, Razvan Machine Learning General Relativity and Quantum Cosmology High Energy Physics - Theory We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, with techniques for deep learning and transport theory. Our proposal is to represent the flow of data from a base distribution to some learned distribution using the bulk-to-boundary mapping of scalar fields in AdS. In the language of machine learning, we are representing and augmenting the flow-matching algorithm with AdS physics. Using a checkerboard toy dataset and MNIST, we find that our model achieves faster and higher quality convergence than comparable physics-free flow-matching models. Our method provides a physically interpretable version of flow matching. More broadly, it establishes the utility of AdS physics and geometry in the development of novel paradigms in generative modeling. |
| title | Holographic generative flows with AdS/CFT |
| topic | Machine Learning General Relativity and Quantum Cosmology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2601.22033 |