Navigating the Quantum Resource Landscape of Entropy Vector Space Using Machine Learning and Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909915873804288 |
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| author | Khumalo, Nothando Mehta, Aman Munizzi, William Narang, Prineha |
| author_facet | Khumalo, Nothando Mehta, Aman Munizzi, William Narang, Prineha |
| contents | We present a machine learning framework to study the dynamics of entropy vectors and quantum resources, including entanglement and magic, focusing on violations of entropy inequalities. Using a reinforcement learning agent formulated as a Markov decision process, we identify quantum circuits that optimally navigate the entropy vector space to generate violations of Ingleton's inequality. We complement this approach with a classical optimization algorithm to produce arbitrary numbers of Ingleton-violating states, with tunable degrees of violation, and empirically determine the maximal attainable violation for Ingleton's inequality. Our analysis reveals characteristic patterns of quantum resources that accompany Ingleton violation. A comprehensive statistical analysis shows that Ingleton-violating states occupy sharply-defined, isolated regions of the Hilbert space, and are extremely rare. Together, these results establish a unified computational toolkit for studying entropy vector dynamics, tracking quantum resource evolution, and engineering circuits with controlled information-theoretic features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Navigating the Quantum Resource Landscape of Entropy Vector Space Using Machine Learning and Optimization Khumalo, Nothando Mehta, Aman Munizzi, William Narang, Prineha Quantum Physics High Energy Physics - Theory Computational Physics We present a machine learning framework to study the dynamics of entropy vectors and quantum resources, including entanglement and magic, focusing on violations of entropy inequalities. Using a reinforcement learning agent formulated as a Markov decision process, we identify quantum circuits that optimally navigate the entropy vector space to generate violations of Ingleton's inequality. We complement this approach with a classical optimization algorithm to produce arbitrary numbers of Ingleton-violating states, with tunable degrees of violation, and empirically determine the maximal attainable violation for Ingleton's inequality. Our analysis reveals characteristic patterns of quantum resources that accompany Ingleton violation. A comprehensive statistical analysis shows that Ingleton-violating states occupy sharply-defined, isolated regions of the Hilbert space, and are extremely rare. Together, these results establish a unified computational toolkit for studying entropy vector dynamics, tracking quantum resource evolution, and engineering circuits with controlled information-theoretic features. |
| title | Navigating the Quantum Resource Landscape of Entropy Vector Space Using Machine Learning and Optimization |
| topic | Quantum Physics High Energy Physics - Theory Computational Physics |
| url | https://arxiv.org/abs/2511.16724 |