Operator fidelity susceptibility: an indicator of quantum criticality

We introduce the operator fidelity and propose to use its susceptibility for characterizing the sensitivity of quantum systems to perturbations. Two typical models are addressed: one is the transverse Ising model exhibiting a quantum phase transition, and the other is the one dimensional Heisenberg spin chain with next-nearest-neighbor interactions, which has the degeneracy. It is revealed that the operator fidelity susceptibility is a good indicator of quantum criticality regardless of the system degeneracy.Comment: Four pages, two figure

Tubular modular permanent-magnet machines equipped with quasi-Halbach magnetized magnets - Part II: Armature reaction and design optimization

Using the analytical formulas derived in Part I for predicting the magnetic field distribution, thrust force, and electromotive force of a three-phase tubular modular permanent-magnet machine equipped with quasi-Halbach magnetized magnets, this paper analyzes the armature reaction field, and addresses issues that are pertinent to the design optimization of the machine. It shows that optimal values of the ratio of the axial length of the radially magnetized magnets to the pole pitch exist for both maximum force capability and minimum force ripple. The utility and accuracy of the analytical predictions and design optimization technique are demonstrated on a 9-slot/10-pole machine

Design of a miniature permanent-magnet generator and energy storage system

The paper describes a methodology for optimizing the design and performance of a miniature permanent-magnet generator and its associated energy storage system. It combines an analytical field model, a lumped reluctance equivalent magnetic circuit, and an equivalent electrical circuit. Its utility is demonstrated by means of a case study on a 15-mW, 6000-r/min generator, and the analysis techniques are validated by measurements on a prototype system

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling

Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.This work was supported by the Chinese National Natural Science Foundation under Grant 69684005 and the Chinese National High-Tech Program under Grant 863-511-9609-003, the EPSRC under Grant GR/L81468

Revealing Majorana Fermion states in a superfluid of cold atoms subject to a harmonic potential

We here explore Majorana Fermion states in an s-wave superfluid of cold atoms in the presence of spin-orbital coupling and an additional harmonic potential. The superfluid boundary is induced by a harmonic trap. Two locally separated Majorana Fermion states are revealed numerically based on the self-consistent Bogoliubov-de Gennes equations. The local density of states are calculated, through which the signatures of Majorana excitations may be indicated experimentally