Descubriendo el potencial de la computación cuántica

@pre-print{nokey,

title = {Machine Learning for maximizing the memristivity of single and coupled quantum memristors},

author = {Hernani-Morales, C. and Alvarado, G. and Albarrán-Arriagada, F. and Vives-Gilabert, Y. and Solano, E. and Martín-Guerrero, J.D. },

url = {https://quantumspain-project.es/wp-content/uploads/2023/09/2309.05062.pdf},

doi = { https://doi.org/10.48550/arXiv.2309.05062},

year  = {2023},

date = {2023-09-10},

urldate = {2023-09-10},

abstract = {We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

},

keywords = {machine learning, UV},

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}

}