PBG Cavity in NV-Diamond for Large Scale Type II Quantum Computing

2004 ◽  
Author(s):  
Selim Shahriar
Keyword(s):  
Quantum Computing ◽  
Large Scale ◽  
Type Ii ◽  
Scale Type

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

Promising routes to large-scale photonic quantum computing

Scilight ◽  
2019 ◽  
Vol 2019 (24) ◽  
pp. 240007
Author(s):  
Meeri Kim
Keyword(s):  
Quantum Computing ◽  
Large Scale

scholarly journals Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mingyue Xue ◽  
Yinxia Su ◽  
Chen Li ◽  
Shuxia Wang ◽  
Hua Yao
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Tree ◽  
Type Ii Diabetes ◽  
Large Scale ◽  
Systolic Pressure ◽  
Gradient Boosting ◽  
Significant Feature ◽  
Type Ii ◽  
Extreme Gradient Boosting

Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

scholarly journals Dynamics of Type-II Supernovae

1996 ◽  
Vol 145 ◽  
pp. 109-117 ◽  
Author(s):  
H.-Th. Janka ◽  
E. M. Müller
Keyword(s):  
Large Scale ◽  
Energy Deposition ◽  
Supernova Explosion ◽  
Threshold Value ◽  
Neutrino Energy ◽  
Two Dimensions ◽  
Iron Core ◽  
Type Ii ◽  
Convective Processes ◽  
Hydrodynamical Simulations

Hydrodynamical simulations of type-II supernovae in one and two dimensions are performed for the revival phase of the delayed shock by neutrino energy deposition. Starting with a post-collapse model of the 1.31 Mʘ iron core of a 15 Mʘ star immediately after the stagnation of the prompt shock about 10 ms after core bounce, the models are followed for several hundred milliseconds with varied neutrino fluxes from the neutrino sphere. The variation of the neutrino luminosities is motivated by the considerable increase of the neutrino emission due to convective processes inside and close to the neutrino sphere (see Janka 1993), which are driven by negative gradients of entropy and electron concentration left behind by the prompt shock (Burrows & Fryxell 1992, Janka & Müller 1992). The size of this luminosity increase remains to be quantitatively analyzed yet and may require multi-dimensional neutrino transport. However, in the presented simulations the region below the neutrino sphere is cut out and replaced by an inner boundary condition, so that the convective zone is only partially included and the neutrino flows are treated as a freely changeable energy source.For small neutrino luminosities the energy transfer to the matter is insufficient to revive the stalled shock. However, there is a sharp transition to successful explosions, when the neutrino luminosities lie above some ‘threshold value’. Once the shock is driven out and the density and temperature of the matter between neutrino sphere and shock start to decrease during the expansion, suitable conditions for further neutrino energy deposition are maintained, and an explosion results. With the neutrino energy deposition the entropy per nucleon in the region between neutrino sphere and shock grows, and convective overturn will set in. Multi-dimensional simulations show that due to the large pressure scale height a large-scale pattern of up-flows and down-flows with velocities close to the local speed of sound develops. Consequently, cold, postshock material is advected down into the neutrino heating layer close to the neutrino sphere and hot material is transported outwards, thus reducing energy losses by re-emission of neutrinos and increasing the pressure behind the shock. Therefore these convective processes are found to be a very important aid to the delayed supernova explosion. In fact, two-dimensional models explode even in cases where spherically symmetrical computations fail.

scholarly journals Tensile and fracture properties of NiAl/Ni micro-laminated composites prepared by reaction synthesis

2006 ◽  
Vol 21 (5) ◽  
pp. 1141-1149 ◽  
Author(s):  
Hee Y. Kim ◽  
Dong S. Chung ◽  
M. Enoki ◽  
Soon H. Hong
Keyword(s):  
Large Scale ◽  
Volume Fraction ◽  
Laminated Composites ◽  
Relative Strength ◽  
Type I ◽  
Reaction Synthesis ◽  
Type Ii ◽  
Composition Variation ◽  
Gradient Microstructure ◽  
Upward Curvature

The mechanical properties of NiAl/Ni micro-laminated composites with highly gradient microstructure have been investigated. Two types of composites with different gradient microstructures were prepared by reaction synthesis. Intermetallics of type I and type II composites mainly consisted of Al-rich Ni0.45Al0.55 with variable thickness and Ni-rich Ni0.58Al0.42 with similar thickness, respectively. As intermetallic volume fraction increased, yield strength of type II followed the rule-of-mixture well, while that of type I deviated due to the composition variation of intermetallic phases. Fracture toughness of type II was higher than that of type I, and all showed KR curves with upward curvature by large-scale bridging. Even though the relative strength of constituent phases in intermetallic/metal laminates was not constant due to the gradient microstructure, the fracture mode transition showed similar behavior to that of metal/ceramic laminates.

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