Unipolar magnetic field pulses as an advantageous tool for ultrafast operations in superconducting Josephson “atoms”

• Daria V. Popolitova,
• Nikolay V. Klenov,
• Igor I. Soloviev,
• Sergey V. Bakurskiy and
• Olga V. Tikhonova

Beilstein J. Nanotechnol. 2019, 10, 1548–1558, doi:10.3762/bjnano.10.152

• of developments in algorithmic and adiabatic quantum computers, artificial metamaterials, and quantum neural networks. Hence, they seem to be very promising for studies of novel types of fast quantum-state control or initialization [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In this work, we

• ultrafast state initialization for algorithmic quantum computers and quantum neural networks as well as in the fast control of the magnetic properties of media from Josephson meta-atoms. (a) The potential energy and the eigenfunctions (with energies E1 ,E2, E3, E4) of the three-junction qubit (described in

Beyond Moore’s technologies: operation principles of a superconductor alternative

• Igor I. Soloviev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Mikhail Yu. Kupriyanov,
• Alexander L. Gudkov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2017, 8, 2689–2710, doi:10.3762/bjnano.8.269

• mentioned localization of information and high non-linearity of Josephson junctions make superconductor circuits to be ideally suited for the implementation of unconventional computational paradigms like cellular automata [94][95], artificial neural networks [96][97][98] or quantum computing [99][100][101

Optical techniques for cervical neoplasia detection

• Tatiana Novikova

Beilstein J. Nanotechnol. 2017, 8, 1844–1862, doi:10.3762/bjnano.8.186

• watchful waiting accompanied by HPV/Pap tests or active treatment is further needed [34][35][36][37][38]. The use of spectra classification algorithms (e.g., Bayesian variable selection, neural networks, library approach, multivariate statistical analysis) may bring its own set of the problems: high

Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• . We optimize their parameters for application in three-layer perceptron and radial basis function networks. Keywords: adiabatic superconductor cells; artificial neural networks; energy efficiency; Josephson effect; superconductivity; Findings Artificial neural networks (ANNs) are famous for their

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

• Christoph Nick,
• Sandeep Yadav,
• Ravi Joshi,
• Christiane Thielemann and
• Jörg J. Schneider

Beilstein J. Nanotechnol. 2014, 5, 1575–1579, doi:10.3762/bjnano.5.169

• between the aggregated networks. These results are in line with the results after using micro-stamped structures of carbon nanotubes, which led to the self-assembly of neural networks [28] and to the formation of neural networks on islands of unordered CNTs [12]. After 21 days in vitro the neural network