Robust quantum control by shaped pulse

Considering the problem of the control of a two-state quantum system by an external field, we establish a general and versatile method that allows the derivation of smooth pulses, suitable for ultrafast applications, that feature the properties of high-fidelity, robustness, and low area. Such shaped pulses can be viewed as a single-shot generalization of the composite pulse technique with a time-dependent phase

Levelable Sets and the Algebraic Structure of Parameterizations

Asking which sets are fixed-parameter tractable for a given parameterization constitutes much of the current research in parameterized complexity theory. This approach faces some of the core difficulties in complexity theory. By focussing instead on the parameterizations that make a given set fixed-parameter tractable, we circumvent these difficulties. We isolate parameterizations as independent measures of complexity and study their underlying algebraic structure. Thus we are able to compare parameterizations, which establishes a hierarchy of complexity that is much stronger than that present in typical parameterized algorithms races. Among other results, we find that no practically fixed-parameter tractable sets have optimal parameterizations

Statistical and Computational Tradeoffs in Stochastic Composite Likelihood

Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy.Comment: 30 pages, 97 figures, 2 author

A Robust Semidefinite Programming Approach to the Separability Problem

We express the optimization of entanglement witnesses for arbitrary bipartite states in terms of a class of convex optimization problems known as Robust Semidefinite Programs (RSDP). We propose, using well known properties of RSDP, several new sufficient tests for the separability of mixed states. Our results are then generalized to multipartite density operators.Comment: Revised version (minor spell corrections) . 6 pages; submitted to Physical Review