Quantum adiabatic machine learning

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. We apply and illustrate this approach in detail to the problem of software verification and validation.Comment: 21 pages, 9 figure

Achieving a quantum smart workforce

Interest in building dedicated Quantum Information Science and Engineering (QISE) education programs has greatly expanded in recent years. These programs are inherently convergent, complex, often resource intensive and likely require collaboration with a broad variety of stakeholders. In order to address this combination of challenges, we have captured ideas from many members in the community. This manuscript not only addresses policy makers and funding agencies (both public and private and from the regional to the international level) but also contains needs identified by industry leaders and discusses the difficulties inherent in creating an inclusive QISE curriculum. We report on the status of eighteen post-secondary education programs in QISE and provide guidance for building new programs. Lastly, we encourage the development of a comprehensive strategic plan for quantum education and workforce development as a means to make the most of the ongoing substantial investments being made in QISE.Comment: 18 pages, 2 figures, 1 tabl

Surface and subsurface composition of the life in the Atacama field sites from rover data and orbital image analysis

The Life in the Atacama project examined six different sites in the Atacama Desert (Chile) over 3 years in an attempt to remotely detect the presence of life with a rover. The remote science team, using only orbital and rover data sets, identified areas with a high potential for life as targets for further inspection by the rover. Orbital data in the visible/near infrared (VNIR) and in the thermal infrared (TIR) were used to examine the mineralogy, geomorphology, and chlorophyll potential of the field sites. Field instruments included two spectrometers (VNIR reflectance and TIR emission) and a neutron detector: this project represents the first time a neutron detector has been used as part of a “science-blind” rover field test. Rover-based spectroscopy was used to identify the composition of small scale features not visible in the orbital images and to improve interpretations of those data sets. The orbital and ground-based data sets produced consistent results, suggesting that much of the field sites consist of altered volcanic terrains with later deposits of sulfates, quartz, and iron oxides. At one location (Site A), the ground-based spectral data revealed considerably greater compositional diversity than was seen from the orbital view. One neutron detector transect provided insight into subsurface hydrogen concentrations, which correlated with life and surface features. The results presented here have implications for targeting strategies, especially for future Mars rover missions looking for potential habitats/paleohabitats