Efficient Schemes for Reducing Imperfect Collective Decoherences

We propose schemes that are efficient when each pair of qubits undergoes some imperfect collective decoherence with different baths. In the proposed scheme, each pair of qubits is first encoded in a decoherence-free subspace composed of two qubits. Leakage out of the encoding space generated by the imperfection is reduced by the quantum Zeno effect. Phase errors in the encoded bits generated by the imperfection are reduced by concatenation of the decoherence-free subspace with either a three-qubit quantum error correcting code that corrects only phase errors or a two-qubit quantum error detecting code that detects only phase errors, connected with the quantum Zeno effect again.Comment: no correction, 3 pages, RevTe

Meta-Stable Brane Configurations with Seven NS5-Branes

We present the intersecting brane configurations consisting of NS-branes, D4-branes(and anti D4-branes) and O6-plane, of type IIA string theory corresponding to the meta-stable nonsupersymmetric vacua in four dimensional N=1 supersymmetric SU(N_c) x SU(N_c') x SU(N_c'') gauge theory with a symmetric tensor field, a conjugate symmetric tensor field and bifundamental fields. We also describe the intersecting brane configurations of type IIA string theory corresponding to the nonsupersymmetric meta-stable vacua in the above gauge theory with an antisymmetric tensor field, a conjugate symmetric tensor field, eight fundamental flavors and bifundamentals. These brane configurations consist of NS-branes, D4-branes(and anti D4-branes), D6-branes and O6-planes.Comment: 34pp, 9 figures; Improved the draft and added some footnotes; Figure 1, footnote 7 and captions of Figures 7,8,9 added or improved and to appear in CQ

Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%