QuTiP: An open-source Python framework for the dynamics of open quantum systems

We present an object-oriented open-source framework for solving the dynamics of open quantum systems written in Python. Arbitrary Hamiltonians, including time-dependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of master equation or Monte-Carlo solvers. We give an overview of the basic structure for the framework before detailing the numerical simulation of open system dynamics. Several examples are given to illustrate the build up to a complete calculation. Finally, we measure the performance of our library against that of current implementations. The framework described here is particularly well-suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal for use in classroom instruction.Comment: 16 pages, 12 figure

Stan: A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting

NASA Thesaurus supplement: A four part cumulative supplement to the 1988 edition of the NASA Thesaurus (supplement 3)

The four-part cumulative supplement to the 1988 edition of the NASA Thesaurus includes the Hierarchical Listing (Part 1), Access Vocabulary (Part 2), Definitions (Part 3), and Changes (Part 4). The semiannual supplement gives complete hierarchies and accepted upper/lowercase forms for new terms

Graphics Processing Unit Acceleration Of Computational Electromagnetic Methods

The use of Graphical Processing Units (GPU\u27s) for scientific applications has been evolving and expanding for the decade. GPU\u27s provide an alternative to the CPU in the creation and execution of the numerical codes that are often relied upon in to perform simulations in computational electromagnetics. While originally designed purely to display graphics on the users monitor, GPU\u27s today are essentially powerful floating point co-processors that can be programmed not only to render complex graphics, but also perform the complex mathematical calculations often encountered in scientific computing. Currently the GPU\u27s being produced often contain hundreds of separate cores able to access large amounts of high-speed dedicated memory. By utilizing the power offered by such a specialized processor, it is possible to drastically speed up the calculations required in computational electromagnetics. This increase in speed allows for the use of GPU based simulations in a variety of situations that the computational time has heretofore been a limiting factor in, such as in educational courses. Many situations in teaching electromagnetics often rely upon simple examples of problems due to the simulation times needed to analyze more complex problems. The use of GPU based simulations will be shown to allow demonstrations of more advanced problems than previously alloby adapting the methods for use on the GPU. Modules will be developed for a wide variety of teaching situations utilizing the speed of the GPU to demonstrate various techniques and ideas previously unrealizable