Identification of Quantum Change Points for Hamiltonians.

Detecting sudden changes in the environment is crucial in many statistical applications. We mainly focus on identifying sudden changes in weak signals transmitted by electromagnetic or gravitational waves. Assuming that the Hamiltonians representing the signals before and after the change are known, we aim to find a discrimination strategy that can detect the change point with the best possible accuracy.

Simple Upper and Lower Bounds on the Ultimate Success Probability for Discriminating Arbitrary Finite-Dimensional Quantum Processes.

We consider the problem of discriminating finite-dimensional quantum processes, also called quantum supermaps, that can consist of multiple time steps. Obtaining the ultimate performance for discriminating quantum processes is of fundamental importance, but is challenging mainly due to the necessity of considering all discrimination strategies allowed by quantum mechanics, including entanglement-assisted strategies and adaptive strategies. In the case in which the processes to be discriminated have internal memories, the ultimate performance would generally be more difficult to analyze.

Evolutionary computation applied to the reconstruction of 3-D surface topography in the SEM.

A genetic algorithm has been applied to the line profile reconstruction from the signals of the standard secondary electron (SE) and/or backscattered electron detectors in a scanning electron microscope. This method solves the topographical surface reconstruction problem as one of combinatorial optimization. To extend this optimization approach for three-dimensional (3-D) surface topography, this paper considers the use of a string coding where a 3-D surface topography is represented by a set of coordinates of vertices.