Phase-locking matter-wave interferometer of vortex states

Matter-wave interferometer of ultracold atoms with different linear momenta has been extensively studied in theory and experiment. The vortex matter-wave interferometer with different angular momenta is applicable as a quantum sensor for measuring the rotation, interatomic interaction, geometric phase, etc. Here we report the first experimental realization of a vortex matter-wave interferometer by coherently transferring the optical angular momentum to an ultracold Bose condensate. After producing a lossless interferometer with atoms only populating the two spin states, we demonstrate that the phase difference between the interferences in the two spin states is locked on $\pi$. We also demonstrate the robustness of this out-of-phase relation, which is independent of the angular-momentum difference between the two interfering vortex states, constituent of Raman optical fields and expansion of the condensate. The experimental results agree well with the calculation from the unitary evolution of wave packet in quantum mechanics. This work opens a new way to build a quantum sensor and measure the atomic correlation in quantum gases.Comment: 5 figure

Expansion dynamics of a spherical Bose-Einstein condensate

We experimentally and theoretically observe the expansion behaviors of a spherical Bose-Einstein condensate. A rubidium condensate is produced in an isotropic optical dipole trap with an asphericity of 0.037. We measure the variation of the condensate size during the expansion process. The free expansion of the condensate is isotropic, which is different from that of the condensate usually produced in the anisotropic trap. The expansion in the short time is speeding and then after a long time the expansion velocity asymptotically approaches a constant value. We derive an analytic solution of the expansion behavior based on the spherical symmetry, allowing a quantitative comparison with the experimental measurement. The interaction energy of the condensate is gradually converted into the kinetic energy at the beginning of the expansion and the kinetic energy dominates after a long-time expansion. We obtain the interaction energy of the condensate in the trap by probing the expansion velocity, which is consistent with the theoretical prediction.Comment: 6 pages, 5 figure

A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO2, Cu2O, Ga2O3, Fe2O3, In2O3, CdO, CeO2, and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

Attribute reduction is a challenging problem in rough set theory, which has been applied in many research fields, including knowledge representation, machine learning, and artificial intelligence. The main objective of attribute reduction is to obtain a minimal attribute subset that can retain the same classification or discernibility properties as the original information system. Recently, many attribute reduction algorithms, such as positive region preservation, generalized decision preservation, and distribution preservation, have been proposed. The existing attribute reduction algorithms for generalized decision preservation are mainly based on the discernibility matrix and are, thus, computationally very expensive and hard to use in large-scale and high-dimensional data sets. To overcome this problem, we introduce the similarity degree for generalized decision preservation. On this basis, the inner and outer significance measures are proposed. By using heuristic strategies, we develop two quick reduction algorithms for generalized decision preservation. Finally, theoretical and experimental results show that the proposed heuristic reduction algorithms are effective and efficient