Laboratory Transferability of Optimally Shaped Laser Pulses for Quantum Control

Optimal control experiments can readily identify effective shaped laser pulses, or "photonic reagents", that achieve a wide variety of objectives. For many practical applications, an important criterion is that a particular photonic reagent prescription still produce a good, if not optimal, target objective yield when transferred to a different system or laboratory, {even if the same shaped pulse profile cannot be reproduced exactly. As a specific example, we assess the potential for transferring optimal photonic reagents for the objective of optimizing a ratio of photoproduct ions from a family of halomethanes through three related experiments.} First, applying the same set of photonic reagents with systematically varying second- and third-order chirp on both laser systems generated similar shapes of the associated control landscape (i.e., relation between the objective yield and the variables describing the photonic reagents). Second, optimal photonic reagents obtained from the first laser system were found to still produce near optimal yields on the second laser system. Third, transferring a collection of photonic reagents optimized on the first laser system to the second laser system reproduced systematic trends in photoproduct yields upon interaction with the homologous chemical family. Despite inherent differences between the two systems, successful and robust transfer of photonic reagents is demonstrated in the above three circumstances. The ability to transfer photonic reagents from one laser system to another is analogous to well-established utilitarian operating procedures with traditional chemical reagents. The practical implications of the present results for experimental quantum control are discussed

A note on the asymptotics for the randomly stopped weighted sums

Let {Xi , i ⩾ 1} be a sequence of identically distributed real-valued random variables with common distribution FX; let {θi , i ⩾ 1} be a sequence of identically distributed, nonnegative and nondegenerate at zero random variables; and let τ be a positive integer-valued counting random variable. Assume that {Xi , i ⩾ 1}, {θi , i ⩾ 1} and τ are mutually independent. In the presence of heavy-tailed Xi's, this paper investigates the asymptotic tail behavior for the maximum of randomly weighted sums Mτ = max1 ⩽ k ⩽ τ ∑ki = 1θi Xi under the condition that {θi , i ⩾ 1} satisfy a general dependence structure

Smoothing Proximal Gradient Method for General Structured Sparse Learning

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the l1/l2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsity-inducing penalties. Our approach is based on a general smoothing technique of Nesterov. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method.Comment: arXiv admin note: substantial text overlap with arXiv:1005.471