Smeared d-Wave Anisotropy in a Monolayer Organic Superconductor

2018 ◽  
Vol 5 (1) ◽  
pp. 1800247 ◽  
Author(s):  
Abdou Hassanien
Keyword(s):  
Organic Superconductor ◽  
D Wave

scholarly journals Prediction of pseudogap formation due to d-wave bond-order in organic superconductor κ−(BEDT-TTF)2X

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Rina Tazai ◽  
Youichi Yamakawa ◽  
Masahisa Tsuchiizu ◽  
Hiroshi Kontani
Keyword(s):  
Bond Order ◽  
Organic Superconductor ◽  
D Wave

Thermodynamic Evidence of d-Wave Superconductivity of the Organic Superconductor λ-(BETS)2GaCl4

2016 ◽  
Vol 85 (4) ◽  
pp. 043705 ◽  
Author(s):  
Shusaku Imajo ◽  
Naoki Kanda ◽  
Satoshi Yamashita ◽  
Hiroki Akutsu ◽  
Yasuhiro Nakazawa ◽  
...  
Keyword(s):  
Organic Superconductor ◽  
D Wave

Isotope effect in the organic superconductor βH-(BEDT-TTF)2I3 where BEDT-TTF is bis (ethylenedithiotetrathiafulvalene)

1993 ◽  
Vol 3 (3) ◽  
pp. 871-885 ◽  
Author(s):  
P. Auban-Senzier ◽  
C. Bourbonnais ◽  
D. Jérome ◽  
C. Lenoir ◽  
P. Batail ◽  
...  
Keyword(s):  
Isotope Effect ◽  
Organic Superconductor

MAGNETIC SUSCEPTIBILITY AND ANISOTROPY OF THE ORGANIC SUPERCONDUCTOR (TMTSF)2ClO4

1983 ◽  
Vol 44 (C3) ◽  
pp. C3-893-C3-901 ◽  
Author(s):  
M. Miljak ◽  
J. R. Cooper ◽  
K. Bechgaard
Keyword(s):  
Magnetic Susceptibility ◽  
Organic Superconductor

scholarly journals D wave bottomonia production from Zb(′) decay

2020 ◽  
Vol 102 (11) ◽  
Author(s):  
Qi Wu ◽  
Dian-Yong Chen ◽  
Takayuki Matsuki
Keyword(s):  
D Wave

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

d-wave superconductor as a model of high-Tcsuperconductors

1994 ◽  
Vol 49 (2) ◽  
pp. 1397-1402 ◽  
Author(s):  
Hyekyung Won ◽  
Kazumi Maki
Keyword(s):  
D Wave
Sign in / Sign up

Export Citation Format

Share Document