Publications by authors named "Igor Soloviev"

In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.

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We have studied the proximity effect in an SF1S1F2s superconducting spin valve consisting of a massive superconducting electrode (S) and a multilayer structure formed by thin ferromagnetic (F1,2) and superconducting (S1, s) layers. Within the framework of the Usadel equations, we have shown that changing the mutual orientation of the magnetization vectors of the F1,2 layers from parallel to antiparallel serves to trigger superconductivity in the outer thin s-film. We studied the changes in the pair potential in the outer s-film and found the regions of parameters with a significant spin-valve effect.

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Josephson digital or analog ancillary circuits are an essential part of a large number of modern quantum processors. The natural candidate for the basis of tuning, coupling, and neromorphic co-processing elements for processors based on flux qubits is the adiabatic (reversible) superconducting logic cell. Using the simplest implementation of such a cell as an example, we have investigated the conditions under which it can optionally operate as an auxiliary qubit while maintaining its "classical" neural functionality.

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The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump.

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We have theoretically studied the transport properties of the SIsNSOF structure consisting of thick (S) and thin (s) films of superconductor, an insulator layer (I), a thin film of normal metal with spin-orbit interaction (SOI) (NSO), and a monodomain ferromagnetic layer (F). The interplay between superconductivity, ferromagnetism, and spin-orbit interaction allows the critical current of this Josephson junction to be smoothly varied over a wide range by rotating the magnetization direction in the single F-layer. We have studied the amplitude of the spin valve effect and found the optimal ranges of parameters.

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In this paper, we present a theoretical study of electronic transport in planar Josephson Superconductor-Normal Metal-Superconductor (SN-N-NS) bridges with arbitrary transparency of the SN interfaces. We formulate and solve the two-dimensional problem of finding the spatial distribution of the supercurrent in the SN electrodes. This allows us to determine the scale of the weak coupling region in the SN-N-NS bridges, i.

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We theoretically investigated the proximity effect in SNSOF and SF'F structures consisting of a superconductor (S), a normal metal (NSO), and ferromagnetic (F',F) thin films with spin-orbit interaction (SOI) in the NSO layer. We show that a normal layer with spin-orbit interaction effectively suppresses triplet correlations generated in a ferromagnetic layer. Due to this effect, the critical temperature of the superconducting layer in the SNSOF multilayer turns out to be higher than in a similar multilayer without spin-orbit interaction in the N layer.

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We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations.

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Made of a thin non-superconducting metal (N) sandwiched by two superconductors (S), SNS Josephson junctions enable novel quantum functionalities by mixing up the intrinsic electronic properties of N with the superconducting correlations induced from S by proximity. Electronic properties of these devices are governed by Andreev quasiparticles (Andreev, A. 1965, 20, 1490) which are absent in conventional SIS junctions whose insulating barrier (I) between the two S electrodes owns no electronic states.

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Thin films of diluted magnetic alloys are widely used in superconducting spintronics devices. Most studies rely on transport measurements and assume homogeneous magnetic layers. Here we examine on a local scale the electronic properties of the well-known two-layer superconductor/ferromagnet structure Nb/CuNi.

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The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron.

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High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons-high-performance, energy-efficient, and compact elements of neuromorphic processor.

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Article Synopsis
  • The research investigates the proximity effect in superconductor/ferromagnetic superlattices, focusing on how variations in ferromagnetic layer thickness and coercive fields affect superconductivity.
  • Using the Usadel equations, the study identifies conditions under which the magnetic alignment of adjacent ferromagnetic layers leads to significant changes in the superconducting order parameter.
  • Experimental observations show that the resistive transition of a Nb/Co multilayer exhibits multiple steps, indicating that local magnetization affects superconductive behavior, suggesting potential applications in tunable kinetic inductors for artificial neural networks.
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Article Synopsis
  • A theoretical framework is proposed to understand how ultrafast population transfer and magnetization reversal in superconducting meta-atoms occur when exposed to short magnetic field pulses.
  • A method using stimulated Raman Λ-type transitions is suggested to enable rapid quantum operations on the picosecond timeframe.
  • An experimental setup for implementing this ultrafast control within a circuit-on-chip is also introduced.
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We present a study of magnetic structures with controllable effective exchange energy for Josephson switches and memory applications. As a basis for a weak link we propose to use a periodic structure composed of ferromagnetic (F) layers spaced by thin superconductors (s). Our calculations based on the Usadel equations show that switching from parallel (P) to antiparallel (AP) alignment of neighboring F layers can lead to a significant enhancement of the critical current through the junction.

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The predictions of Moore's law are considered by experts to be valid until 2020 giving rise to "post-Moore's" technologies afterwards. Energy efficiency is one of the major challenges in high-performance computing that should be answered. Superconductor digital technology is a promising post-Moore's alternative for the development of supercomputers.

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Article Synopsis
  • The paper suggests using superconducting quantum interferometers to create neural networks that consume very little power.
  • These networks utilize special components called Josephson cells, which have activation functions shaped like sigmoid and Gaussian curves.
  • The authors focus on optimizing these components for popular types of neural networks, specifically three-layer perceptrons and radial basis function networks.
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We present our approach for a consistent, fully quantum mechanical description of the magnetization reversal process in natural and artificial atomic systems by means of short magnetic pulses. In terms of the simplest model of a two-level system with a magnetic moment, we analyze the possibility of a fast magnetization reversal on the picosecond timescale induced by oscillating or short unipolar magnetic pulses. We demonstrate the possibility of selective magnetization reversal of a superconducting flux qubit using a single flux quantum-based pulse and suggest a promising, rapid Λ-scheme for resonant implementation of this process.

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Background: An experimental and theoretical study of a silicon-nanowire field-effect transistor made of silicon on insulator by CMOS-compatible methods is presented.

Results: A maximum Nernstian sensitivity to pH change of 59 mV/pH was obtained experimentally. The maximum charge sensitivity of the sensor was estimated to be on the order of a thousandth of the electron charge in subthreshold mode.

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