Machine learning approach to reconstruct density matrices from quantum marginals
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| Publicado en: | Machine Learning : Science and Technology vol. 6, no. 2 (Jun 2025), p. 025068 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
IOP Publishing
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | In this work, we propose a machine learning (ML)-based approach to address a specific aspect of the Quantum Marginal Problem: reconstructing a global density matrix compatible with a given set of quantum marginals. Our method integrates a quantum marginal imposition technique with convolutional denoising autoencoders. The loss function is carefully designed to enforce essential physical constraints, including Hermiticity, positivity, and normalization. Through extensive numerical simulations, we demonstrate the effectiveness of our approach, achieving high success rates and accuracy. Furthermore, we show that, in many cases, our model offers a faster alternative to state-of-the-art semidefinite programming solvers without compromising solution quality. These results highlight the potential of ML techniques for solving complex problems in quantum mechanics. |
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| ISSN: | 2632-2153 |
| DOI: | 10.1088/2632-2153/ade48d |
| Fuente: | Science Database |