Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit

Geometric quantum manipulation and Landau-Zener interferometry have been separately explored in many quantum systems. In this Letter, we combine these two approaches to study the dynamics of a superconducting phase qubit. We experimentally demonstrate Landau-Zener interferometry based on the pure geometric phases in this solid-state qubit. We observe the interference caused by a pure geometric phase accumulated in the evolution between two consecutive Landau-Zener transitions, while the dynamical phase is canceled out by a spin-echo pulse. The full controllability of the qubit state as a function of the intrinsically robust geometric phase provides a promising approach for quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia

Rapid characterization of microscopic two-level systems using Landau-Zener transitions in a superconducting qubit

This is the published version. Copyright 2015 American Institute of PhysicsWe demonstrate a fast method to detect microscopic two-level systems in a superconducting phase qubit. By monitoring the population leak after sweeping the qubit bias flux, we are able to measure the two-level systems that are coupled with the qubit. Compared with the traditional method that detects two-level systems by energy spectroscopy, our method is faster and more sensitive. This method supplies a useful tool to investigate two-level systems in solid-state qubits

Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus)

[EN] Skin is an important trait for Rex rabbits and skin development is influenced by many processes, including hair follicle cycling, keratinocyte differentiation and formation of coat colour and skin morphogenesis. We identified differentially expressed microRNAs (miRNAs) between the back and belly skin in Rex rabbits. In total, 211 miRNAs (90 upregulated miRNAs and 121 downregulated miRNAs) were identified with a |log2 (fold change)|>1 and P-value<0.05. Using target gene prediction for the miRNAs, differentially expressed predicted target genes were identified and the functional enrichment and signalling pathways of these target genes were processed to reveal their biological functions. A number of differentially expressed miRNAs were found to be involved in regulation of the cell cycle, skin epithelium differentiation, keratinocyte proliferation, hair follicle development and melanogenesis. In addition, target genes regulated by miRNAs play key roles in the activities of the Hedgehog signalling pathway, Wnt signalling pathway, Osteoclast differentiation and MAPK pathway, revealing mechanisms of skin development. Nine candidate miRNAs and 5 predicted target genes were selected for verification of their expression by quantitative reverse transcription polymerase chain reaction. A regulation network of miRNA and their target genes was constructed by analysing the GO enrichment and signalling pathways. Further studies should be carried out to validate the regulatory relationships between candidate miRNAs and their target genes.This study was supported by the Modern Agricultural Industrial System Special Funding (CARS-44-A-1), the Priority Academic Programme Development of Jiangsu Higher Education Institutions (2014-134) and the General Programme of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJB230001).Zhao, B.; Chen, Y.; Mu, L.; Hu, S.; Wu, X. (2018). Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus). World Rabbit Science. 26(2):179-190. https://doi.org/10.4995/wrs.2018.7058SWORD179190262Adamidi C. 2008. Discovering microRNAs from deep sequencing data using miRDeep. Nature Biotechnol., 26: 407-415. https://doi.org/10.1038/nbt1394Adijanto J., Castorino J.J., Wang Z.X., Maminishkis A., Grunwald G.B., Philp N.J. 2012. 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Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Neural models have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features. Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models. In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss. We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones. We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels. Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.Comment: NIPS 201