G-AQFS: grid computing exploitation for the management of air quality in presence of complex meteorological circulations
Leveraging grid computing technology, i.e. the visualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, we present a Grid Air Quality Forecast System (G-AQFS). The modeling system consists of meteorological and dispersion models coupled in cascade. The computational workflow of the modeling system is defined by means of DAGs (direct acyclic graph). A simple system is presented to manage and schedule the computational grid resources. As case study the system has been applied over Salento area, in the Apulia region (South-eastern Italy), to simulate ground level ozone concentration. Model predictions have been compared with field measurements, with reasonable results.
In this paper, we show how genetic programming (GP) can be used to evolve system-size-independent quantum algorithms, and present a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.