Descubriendo el potencial de la computación cuántica

@article{nokey,

title = {Nuclear shell-model simulation in digital quantum computers},

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

url = {https://www.nature.com/articles/s41598-023-39263-7},

doi = {doi.org/10.1038/s41598-023-39263-7},

year  = {2023},

date = {2023-07-29},

urldate = {2023-02-07},

journal = {Scientific Reports},

volume = {13},

abstract = {The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states that circumvents this limitation by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus and configuration space. Our work paves the way for quantum computing shell-model studies across the nuclear chart.},

keywords = {algorithms, quantic, quantum computing, simulations},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum Brain Networks: A Perspective},

author = {Miranda, E. R. and Martín-Guerrero, J. D. and Venkatesh, S. and Hernani-Morales, C. and Lamata, L. and Solano, E. },

editor = {Durdu Guney},

url = {https://www.mdpi.com/2079-9292/11/10/1528/htm},

doi = {10.3390/electronics11101528},

year  = {2022},

date = {2022-05-11},

urldate = {2022-05-11},

journal = {Electronics },

volume = {11},

number = {10},

pages = {1528},

abstract = {We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business. },

keywords = {artificial intelligence, quantum computing, UV},

pubstate = {published},

tppubtype = {article}

}