Aplicación

@article{nokey,

title = {Benefits of Open Quantum Systems for Quantum Machine Learning},

author = {Olivera-Atencio, M.L. and Lamata, L. and Casado-Pascual, J.



},

url = {https://quantumspain-project.es/wp-content/uploads/2023/12/Adv-Quantum-Tech-2023-Olivera‐Atencio-Benefits-of-Open-Quantum-Systems-for-Quantum-Machine-Learning.pdf},

doi = {10.1002/qute.202300247},

isbn = {2511-9044},

year  = {2023},

date = {2023-12-10},

urldate = {2023-12-10},

journal = {Adv Quantum Technologies},

abstract = {Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. However, dissipationand noise arising from the coupling with the environment are commonlyperceived as major obstacles to its practical exploitation, as they impact thecoherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming toharness the potential of noise and dissipation instead of combating them.Surprisingly, it is shown that these seemingly detrimental factors can providesubstantial advantages in the operation of QML algorithms under certaincircumstances. Exploring and understanding the implications of adaptingQML algorithms to open quantum systems opens up pathways for devisingstrategies that effectively leverage noise and dissipation. The recent worksanalyzed in this perspective represent only initial steps toward uncoveringother potential hidden benefits that dissipation and noise may offer. Asexploration in this field continues, significant discoveries are anticipated thatcould reshape the future of quantum computing.},

keywords = {artificial intelligence, Quantum algorithms, quantum machine learning, US},

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}

}