Aplicación

@pre-print{nokey,

title = {Quantum entanglement patterns in the structure of atomic nuclei within the nuclear shell model},

author = {Pérez-Obiol, A. and Masot-Llima, S. and Romero, A. M. and Menéndez, J. and Rios, A. and García-Sáez, A. and Juliá-Díaz, B. },

url = {https://quantumspain-project.es/wp-content/uploads/2023/08/Quantum-entanglement-patterns-in-the-structure-of-atomic-nuclei-within-the-nuclear-shell-model.pdf},

doi = {doi.org/10.48550/arXiv.2307.05197},

year  = {2023},

date = {2023-07-11},

urldate = {2023-07-11},

abstract = {Quantum entanglement offers a unique perspective into the underlying structure of strongly-correlated systems such as atomic nuclei. In this paper, we use quantum information tools to analyze the structure of light and medium mass berillyum, oxygen, neon and calcium isotopes within the nuclear shell model. We use different entanglement metrics, including single-orbital entanglement, mutual information, and von Neumann entropies for different equipartitions of the shell-model valence space and identify mode/entanglement patterns related to the energy, angular momentum and isospin of the nuclear single-particle orbitals. We observe that the single-orbital entanglement is directly related to the number of valence nucleons and the energy structure of the shell, while the mutual information highlights signatures of proton-proton and neutron-neutron pairing. Proton and neutron orbitals are weakly entangled by all measures, and in fact have the lowest von Neumann entropies among all possible equipartitions of the valence space. In contrast, orbitals with opposite angular momentum projection have relatively large entropies. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.},

keywords = {quantic},

pubstate = {published},

tppubtype = {pre-print}

}

@pre-print{nokey,

title = {Modern applications of machine learning in quantum sciences},

author = {Anna Dawid and Julian Arnold and Borja Requena and Alexander Gresch and Marcin Płodzień and Kaelan Donatella and Kim Nicoli and Paolo Stornati and Rouven Koch and Miriam Büttner and Robert Okuła and Gorka Muñoz-Gil and Rodrigo A. Vargas-Hernández and Alba Cervera-Lierta and Juan Carrasquilla and Vedran Dunjko and Marylou Gabrié and Patrick Huembeli and Evert van Nieuwenburg and Filippo Vicentini and Lei Wang and Sebastian J. Wetzel and Giuseppe Carleo and Eliška Greplová and Roman Krems and Florian Marquardt and Michał Tomza and Maciej Lewenstein and Alexandre Dauphin},

url = {https://arxiv.org/abs/2204.04198},

doi = {10.48550/arXiv.2204.04198},

year  = {2022},

date = {2022-04-08},

urldate = {2022-04-08},

journal = {Arxiv},

pages = {268},

abstract = {In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.},

keywords = {machine learning, quantic, quantum science, quantumsimulation},

pubstate = {published},

tppubtype = {pre-print}

}