Probing the superconducting pairing symmetry from spin excitations in BiS2_2 based superconductors

Starting from a two-orbital model and based on the random phase approximation, spin excitations in the superconducting state of the newly discovered BiS$_2$ superconductors with three possible pairing symmetries are studied theoretically. We show that spin response is uniquely determined by the pairing symmetry. Possible spin resonance excitations might occur for the d-wave symmetry at an incommensurate momentum about $(0.7\pi,0.7\pi)$. For the p-wave symmetry the transverse spin excitation near $(0,0)$ is enhanced. For the s-wave pairing symmetry there is no spin resonance signature. These distinct features may be used for probing or determining the pairing symmetry in this newly discovered compound.Comment: 4 pages, 5 figure

Experimental NMR Realization of A Generalized Quantum Search Algorithm

A generalized quantum search algorithm, where phase inversions for the marked state and the prepared state are replaced by $\pi/2$ phase rotations, is realized in a 2-qubit NMR heteronuclear system. The quantum algorithm searches a marked state with a smaller step compared to standard Grover algorithm. Phase matching requirement in quantum searching is demonstrated by comparing it with another generalized algorithm where the two phase rotations are $\pi/2$ and $3\pi/2$ respectively. Pulse sequences which include non 90 degree pulses are given.Comment: 12 pages, 2 figures, accepted for publication in Plysics Letters

Finding the influence set through skylines

Given a set P of products, a set O of customers, and a product p ∈ P, a bichromatic reverse skyline query retrieves all the customers in O that do not find any other product in P to be absolutely better than p. More specifically, a customer o ∈ O is in the reverse skyline of p ∈ P if and only no other product in P better matches the preference of o on all dimensions. The only existing bichromatic reverse skyline algorithm, which we refer to as basic, is designed for uncertain data. This paper focuses on traditional datasets, where each object is a precise point. Since a precise point can be regarded as a special uncertain object, basic can still be applied. However, as precise data are inherently easier to handle than uncertain data, one should expect that basic can be further improved by taking advantage of the reduced problem complexity. Indeed, we observe several non-trivial heuristics that can optimize the access order to achieve stronger pruning power. Motivated by this, we propose a new algorithm called BRS, and prove that BRS never entails more I/Os than basic. Besides our theoretical analysis, we also perform extensive experiments to show that in practice BRS usually outperforms basic by a large factor. For example, when both P and O follow the anti-correlated distribution, BRS is faster than basic by an order of magnitude. Finally, we address a new variation of bichromatic reverse skyline search where the conventional definition of dynamic skylines no longer makes sense. Copyright 2009 ACM