The aim of presented research is to design a nanodevice, based on a MoS2 monolayer, performing operations on a well-defined valley qubit. We show how to confine an electron in a gate induced quantum dot within the monolayer, and to perform the NOT operation on its valley degree of freedom. The operations are carried out all electrically via modulation of the confinement potential by oscillating voltages applied to the local gates. Such quantum dot structure is modeled realistically. Through these simulations we investigate the possibility of realization of a valley qubit in analogy with a realization of the spin qubit. We accurately model the potential inside the nanodevice accounting for proper boundary conditions on the gates and space-dependent materials permittivity by solving the generalized Poisson's equation. The time-evolution of the system is supported by realistic self-consistent Poisson-Schrödinger tight-binding calculations. The tight-binding calculations are further confirmed by simulations within the effective continuum model.
The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18 000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras. All the images are classified into countable, uncountable, and empty, with the countable class labeled by microbiologists with colony location and species identification (336 442 colonies in total). This study describes the dataset itself and the process of its development. In the second part, the performance of selected deep neural network architectures for object detection, namely Faster R-CNN and Cascade R-CNN, was evaluated on the AGAR dataset. The results confirmed the great potential of deep learning methods to automate the process of microbe localization and classification based on Petri dish photos. Moreover, AGAR is the first publicly available dataset of this kind and size and will facilitate the future development of machine learning models. The data used in these studies can be found at https://agar.neurosys.com/.
A novel method and nanodevice are introduced that allows to rotate the single electron spin confined in a gated electrostatic InSb nanowire quantum dot. Proposed method does not require application of any (oscillating or static) external magnetic fields. Our proposal instead employs spatial and time modulation of confining potential induced by electric gates, which, in turn leads to oscillating Rashba type spin-orbit coupling. Moving electron back and forth in such a variable Rashba field allows for realization of spin rotations around two different axes separately without using an external magnetic field. The results are supported by realistic three-dimensional time dependent Poisson-Schrödinger calculations for systems and material parameters corresponding to experimentally accessible structures.
A nanodevice capable of separating spins of two electrons confined in a quantum dot formed in a gated semiconductor nanowire is proposed. Two electrons confined initially in a single quantum dot in the singlet state are transformed into the system of two electrons confined in two spatially separated quantum dots with opposite spins. In order to separate the electrons' spins we exploit transitions between the singlet and the triplet state, which are induced by resonantly oscillating Rashba spin-orbit coupling strength. The proposed device is all electrically controlled and the electron spin separation can be realized within tens of picoseconds. The results are supported by solving numerically the quasi-one-dimensional time-dependent Schroedinger equation for two electrons, where the electron-electron correlations are taken into account in the exact manner.
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