scholarly journals Quasi-particle Dissipation in d-wave Superconductor Phase Qubit

2004 ◽  
Author(s):  
Shiro Kawabata
Keyword(s):  
Quasi Particle ◽  
Phase Qubit ◽  
D Wave

scholarly journals Theories of low-energy quasi-particle states in disordered d-wave superconductors

2002 ◽  
Vol 359 (4) ◽  
pp. 283-354 ◽  
Author(s):  
Alexander Altland ◽  
B.D. Simons ◽  
M.R. Zirnbauer
Keyword(s):  
Low Energy ◽  
Quasi Particle ◽  
D Wave

Quantum limit of quasi-particle spectrum in the vortex states of d-wave superconductors

2000 ◽  
Vol 341-348 ◽  
pp. 1121-1122 ◽  
Author(s):  
Masaru Kato ◽  
Hiroyuki Yuzuriha ◽  
Kazumi Maki
Keyword(s):  
Quantum Limit ◽  
Particle Spectrum ◽  
Quasi Particle ◽  
Vortex States ◽  
D Wave

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.

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