The pointer basis and the feedback stabilization of quantum systems

The dynamics for an open quantum system can be `unravelled' in infinitely many ways, depending on how the environment is monitored, yielding different sorts of conditioned states, evolving stochastically. In the case of ideal monitoring these states are pure, and the set of states for a given monitoring forms a basis (which is overcomplete in general) for the system. It has been argued elsewhere [D. Atkins et al., Europhys. Lett. 69, 163 (2005)] that the `pointer basis' as introduced by Zurek and Paz [Phys. Rev. Lett 70, 1187(1993)], should be identified with the unravelling-induced basis which decoheres most slowly. Here we show the applicability of this concept of pointer basis to the problem of state stabilization for quantum systems. In particular we prove that for linear Gaussian quantum systems, if the feedback control is assumed to be strong compared to the decoherence of the pointer basis, then the system can be stabilized in one of the pointer basis states with a fidelity close to one (the infidelity varies inversely with the control strength). Moreover, if the aim of the feedback is to maximize the fidelity of the unconditioned system state with a pure state that is one of its conditioned states, then the optimal unravelling for stabilizing the system in this way is that which induces the pointer basis for the conditioned states. We illustrate these results with a model system: quantum Brownian motion. We show that even if the feedback control strength is comparable to the decoherence, the optimal unravelling still induces a basis very close to the pointer basis. However if the feedback control is weak compared to the decoherence, this is not the case

Darwinian Data Structure Selection

Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for \emph{every} project in $5$ Java projects from DaCapo benchmark, $8$ popular projects and $30$ uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves \emph{all} measures for $86\%$ ($37/43$) of the projects. The median improvement across the best solutions is $4.8\%$, $10.1\%$, $5.1\%$ for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by $16.5$\%, $1\%$ and $2.2\%$ for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by $23.5$\%. \emph{Every} client of these projects will benefit from these performance improvements.Comment: 11 page

Boundary Layer Stability and Laminar-Turbulent Transition Analysis with Thermochemical Nonequilibrium Applied to Martian Atmospheric Entry

As Martian atmospheric entry vehicles increase in size to accommodate larger payloads, transitional ow may need to be taken into account in the design of the heat shield in order to reduce heat shield mass. The mass of the Thermal Protection System (TPS) comprises a significant portion of the vehicle mass, and a reduction of this mass would result in fuel savings. The current techniques used to design entry shields generally assume fully turbulent flow when the vehicle is large enough to expect transitional flow, and while this worst-case scenario provides a greater factor of safety it may also result in overdesigned TPS and unnecessarily high vehicle mass. Greater accuracy in the prediction of transition would also reduce uncertainty in the thermal and aerodynamic loads. Stability analysis, using e(sup N) -based methods including Linear Stability Theory (LST) and the Parabolized Stability Equations (PSE), offers a physics-based method of transition prediction that has been thoroughly studied and applied in perfect gas flows, and to a more limited extent in reacting and nonequilibrium flows. These methods predict the amplification of a known disturbance frequency and allow identification of the most unstable frequency. Transition is predicted to occur at a critical amplification or N Factor, frequently determined through experiment and empirical correlations. The LAngley Stability and TRansition Analysis Code (LASTRAC), with modifications for thermochemically reacting flows and arbitrary gas mixtures, will be presented with LST results on a simulation of a high enthalpy CO2 gas wind tunnel test relevant to Martian atmospheric entry. The results indicate transition caused by modified Tollmien-Schlichting waves on the leeward side, which are predicted to be more stable and cause transition slightly downstream when thermochemical nonequilibrium is included in the stability analysis for the same mean flow solution