Quantum interference induced photon localisation and delocalisation in coupled cavities

We study photon localisation and delocalisation in a system of two nonlinear cavities with intensity-dependent coupling. It is shown that complete localisation or delocalisation is possible for proper choices of the strengths of nonlinearity, detuning and inter-cavity coupling. Role of the relative phase in the initial superposition in attaining localisation and delocalisation is discussed. Effects of dissipation and decoherence are considered and the use of quantum interference in reducing dissipation is explored. Many of the features of the system are shown to be the consequences of quantum interference.Comment: 18 pages, 10 figure

Quantum information processing in cavities: A review

Processing of information and computation undergoing a paradigmatic shift since the realization of the enormous potential of quantum features to perform these tasks. Coupled cavity array is one of the well-studied systems to carry out these tasks. It is a versatile platform to build quantum networks for distributed information processing and communication. Cavities have the salient feature of retaining photons for longer, thereby enabling them to travel coherently through the array without losing them in dissipation. Several research groups have successfully demonstrated the coupling of cavities and implemented various quantum information protocols. These advancements pave the way for implementing cavity arrays for large scale quantum communications and computations. This article reviews theoretical proposals and discusses a few pertinent experimental realizations of quantum information tasks in cavities.Comment: Suggestions and comments are welcom

Limitations to Realize Quantum Zeno Effect in Beam Splitter Array -- a Monte Carlo Wavefunction Analysis

Effects of non-ideal optical components in realizing quantum Zeno effect in an all-optical setup are analyzed. Beam splitters are the important components in this experimental configuration. Nonuniform transmission coefficient, photon absorption and thermal noise are considered. Numerical simulation of the experiment is performed using the Monte Carlo wavefunction method. It is argued that there is an optimal number of beam splitters to be used for maximizing the expected output in the experiment.Comment: To be published in the Journal of the Physical Society of Japa

KNOWLEDGE MANAGEMENT OF IT PROFESSIONALS IN BENGALURU CITY

This article focuses to explore the composition of “Knowledge Management” of IT professionals in Bengaluru city. The article applies data reduction technique using EFA and Simple Linear Regression on a sample of 317 IT professionals and reduces a set of 13 variables converted into three factors. The current research proposes a model of the impact of Management Support for Knowledge Application on Project Knowledge Application and Project   Knowledge Application on effective Knowledge Management in the IT industry. The results proved that Management Support for Knowledge Application is significantly impacting on Project Knowledge Application and Project Knowledge Application significantly impacting on effective knowledge management. Hence, the HR managers of IT companies’ can improve the knowledge management of IT professionals

CONSTRAINT ROBUST PORTFOLIO SELECTION BY MULTIOBJECTIVE EVOLUTIONARY GENETIC ALGORITHM

The problem of portfolio selection is a very challenging problem in computational finance and has received a lot of attention in last few decades. Selecting an asset and optimal weighting of it from a set of available assets is a critical issue for which the decision maker takes several aspects into consideration. Different constraints like cardinality constraints, minimum buy in thresholds and maximum limit constraint are associated with assets selection. Financial returns associated are often strongly non-Gaussian in character, and exhibit multivariate outliers. Taking these constraints into consideration and with the presence of these outliers we consider a multi-objective problem where the percentage of each available asset is so selected that the total profit of the portfolio is maximized while total risk is minimized. Nondominated Sorting Genetic Algorithm-II is used for solving this multiobjective portfolio selection problem. Performance of the proposed algorithm is carried out by performing different numerical experiments using real-world data

Development of Some Novel Nonlinear and Adaptive Digital Image Filters for Efficient Noise Suppression

Some nonlinear and adaptive digital image filtering algorithms have been developed in this thesis to suppress additive white Gaussian noise (AWGN), bipolar fixed-valued impulse, also called salt and pepper noise (SPN), random-valued impulse noise (RVIN) and their combinations quite effectively. The present state-of-art technology offers high quality sensors, cameras, electronic circuitry: application specific integrated circuits (ASIC), system on chip (SOC), etc., and high quality communication channels. Therefore, the noise level in images has been reduced drastically. In literature, many efficient nonlinear image filters are found that perform well under high noise conditions. But their performance is not so good under low noise conditions as compared to the extremely high computational complexity involved therein. Thus, it is felt that there is sufficient scope to investigate and develop quite efficient but simple algorithms to suppress low-power noise in an image. When..