A quantum‐enhanced implementation of the binary inversion method for gravity data acquisition is discussed. The subsurface structure of a single density anomaly with an assigned density contrast is calculated by using a D‐Wave adiabatic quantum computer. In particular, an iterative heuristic based on quantum annealing that recovers a sharp shape of the subsurface anomaly is developed. Such a task is accomplished by collecting partial images obtained by quantum annealing processes for optimal Lagrange penalty coefficients. The results are compared with those obtained according to the same cost function minimized via genetic algorithms by conventional hardware on a realistic 2D dataset. The outcomes of this work are promising as the reconstructed model is obtained in tenths of iterations instead of the hundreds required in conventional methods. Moreover, for the part of the computation that resides in the quantum processing unit, the computational cost of the single quantum annealing descent is constant with respect to the number of degrees of freedom of the subsurface grid. The implemented method is likely to reveal its full potential on forthcoming quantum annealing devices, outperforming existing techniques.

This paper describes the development activity that has been carried out for a living laboratory for the city of Cagliari aimed at functioning as a learning center for local SMEs willing to improve their skills in IoT and create applications that will be integrated in an open innovation ecosystem. The many users belonging to the various SMEs involved in the project required an ICT laboratory with a platform that could manage them and provide a multi-tenant environment for the development of IoT applications. The architecture also had to be scalable and interoperable, and the resulting platform had to collect many kinds of data from sensors or other data sources, elaborate them, and show georeferenced information on a 3D satellite interactive view along with statistics on side panels. This work was based on a platform already developed by CRS4 for a previous project. Preserving the concept of the decision-making tool for Smart Cities, almost every component was redesigned, and, in this paper, we describe the new solutions that have been implemented. Starting from the former structure, further features were added in a novel way in order to offer an enhanced framework that can deal with the activities of the laboratory, exploiting the scalability of the open-source systems involved, their robustness and flexibility, and leveraging domain standards. In this article, the main challenges involved in the development of the platform are described, as well as the solutions that have been implemented so far.