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It is known that quantum well solar cells (QWSCs) can enhance short circuit current and power conversion efficiency in comparison with similar, conventional solar cells made from the quantum well (QW) barrier material alone. In this article we report measurements of the dark-current and open-circuit voltage (Voc) of a number of quantum well cells in three different lattice-matched material systems, namely, Al0.35Ga0.65As/GaAs, GaInP/GaAs, and InP/InGaAs. We also present the results obtained from comparable control cells without wells formed either from the material of the barriers or the well material alone. Our results clearly demonstrate in all three cases that, at fixed voltage, QWSC dark currents are systematically lower than would be expected from control cells with the same effective absorption edge. Measurements of Voc in a white-light source show that the open-circuit voltages of the QWSCs are higher than those of control cells formed from the well material. Furthermore, this enhancement is more than is expected from the shift in the absorption edge due to the effect of confinement in the wells. We discuss these results in the light of recent theoretical speculation about the upper limit to the efficiency of an ideal quantum well solar cell. We report on a 50 well QWSC with open-circuit voltage higher than the world record conventional cell formed from the well material, namely, GaAs.

The effect of the dislocation line density produced by the relaxation of strain in GaAs/In x Ga 1Ϫx As multiquantum wells where xϭ0.155-0.23 has been studied. There is a strong correlation between the dark line density, observed by cathodoluminescence, before processing of the wafers into photodiode devices, and the subsequent low forward bias ͑Ͻ1.5 V͒ dark current densities of the devices. A comparison is made of the correlation between the reverse bias current density and dark line density and it is found that, in this range of strain, the forward bias current density varies more. Two growth methods, molecular beam epitaxy and metal organic vapor phase epitaxy, have been used to produce the wafers and no difference between the growth methods has been found in dark line or current density variations with strain.

One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC–ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.

Please scroll down for article-it is on subsequent pages With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Ben-Zvi, T. and Carton, T.C.

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