ﻻ يوجد ملخص باللغة العربية
We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing a one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features by both positive and negative signal transfer coefficients in the range ~ (-0.5,0.5). We briefly discuss implementation issues and further steps toward multilayer adiabatic superconducting artificial neural network which promises to be a compact and the most energy-efficient implementation of MLP.
We perform infrared conductivity measurements on a series of CaCuO$_2$/SrTiO$_3$ heterostructures made by the insulating cuprate CaCuO$_2$ (CCO) and the insulating perovkite SrTiO$_3$ (STO). We estimate the carrier density of various heterostructures
We experimentally study a vacuum-induced Autler-Townes doublet in a superconducting three-level artificial atom strongly coupled to a coplanar waveguide resonator and simultaneously to a transmission line. The Autler-Townes splitting is observed in t
Based on recent studies regarding high-temperature (high-$T_c$) La-Y ternary hydrides (e.g., $P{bar{1}}$-La$_2$YH$_{12}$, $Pm{bar{3}}m$-LaYH$_{12}$, and $Pm{bar{3}}m$-(La,Y)H$_{10}$ with a maximum $T_c sim 253$ K), we examined the phase and structura
We study a superconducting artificial atom which is represented by a single Josephson junction or a Josephson junction chain, capacitively coupled to a coherently driven transmission line, and which contains exactly one residual quasiparticle (or up
Prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computati