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

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

Electronic and Band Structure Calculation of Wurtzite CdS Using GGA and GGA+U functionals

The wurtzite (wz) structure of CdS is analyzed using density functional theory within the generalized gradient approximation (GGA) and Hubbard correction (GGA+U). The total energy convergence evaluation is carried out concerning energy cut-off (ecutwfc) and k-point sampling. The geometry optimization of wz-CdS is calculated using the total energy and force minimization process, which is based on the Broyden Fletcher Goldfarb Shanno (BFGS) optimization algorithm. Bulk modulus and lattice parameters are estimated to ensure accuracy of the calculations. The electronic band structure, density of states (DOS), and projected density of states (PDOS) of wz-CdS are analyzed. The band structure calculation shows CdS as direct band gap semiconductor. The electronic correlation in CdS is altered by varying U-parameters of valence orbitals of Cd and S. The alteration of electronic correlation results in convergence of the band gap to the experimental value 2.4 eV. The alteration of U-parameter affects substantially the density of states near the band edges

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..