Generation of Atomic Cluster States through the Cavity Input-Output Process

We propose a scheme to implement a two-qubit controlled-phase gate for single atomic qubits, which works in principle with nearly ideal success probability and fidelity. Our scheme is based on the cavity input-output process and the single photon polarization measurement. We show that, even with the practical imperfections such as atomic spontaneous emission, weak atom-cavity coupling, violation of the Lamb-Dicke condition, cavity photon loss, and detection inefficiency, the proposed gate is feasible for generation of a cluster state in that it meets the scalability criterion and it operates in a conclusive manner. We demonstrate a simple and efficient process to generate a cluster state with our high probabilistic entangling gate

The contribution of molecular interaction potentials to properties and activities of ionic liquid ions in solution

Ionic liquids (ILs) have attracted significant interest due to their beneficial and tuneable physicochemical properties. To experimentally identify the IL structure(s) most suited for a certain technical purpose with no adverse effects to man and the environment, it would result in a nearly insurmountable number of trial and error experiments. Therefore, it is essential to understand their molecular interaction potentials. Thus, experiments were carried out with high performance liquids chromatography to estimate the molecular interaction potentials of 30 cations and 20 anions. An in silico method, i.e. COSMO-calculation, was employed to calculate the descriptors for more conventional and easily accessible approaches. Then using the measured and calculated molecular interaction potentials, it was established prediction models for the physicochemical and biological property of ILs in various environments

Controlled Unitary Operation between Two Distant Atoms

We propose a scheme for implementing a controlled unitary gate between two distant atoms directly communicating through a quantum transmission line. To achieve our goal, only a series of several coherent pulses are applied to the atoms. Our scheme thus requires no ancilla atomic qubit. The simplicity of our scheme may significantly improve the scalability of quantum computers based on trapped neutral atoms or ions

Variational Deep Image Restoration

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction.Comment: IEEE Transactions on Image Processing (TIP 2022

DEVELOPMENT OF A K-NN MODEL TO PREDICT THE POLARITY OF KOREAN GAME REVIEW COMMENTS

The purpose of this paper is to develop the machine learning model to evaluate game software in quantitative scale using opinion mining with the review comments which are provided by users in Korean. To do this, we first decompose the review comments into a lot of meaningful morphemes, and second construct a dictionary for opinion mining. Third, we develop a k-NN model to predict the polarity of review comment. Finally, we predict the polarity for each review comment which is included in validation data set by the model. The experimental results of the developed model are performed by the model which is implemented by JAVA and R language