Quantum Control attempts to apply and extend the principles already used for classical control systems to the quantum domain. In this way we hope to establish a control theory specifically dedicated to regulating quantum systems. This proposal addresses some key problems related to the control of open quantum systems by applying quantum feedback control. Open quantum systems are quantum systems in interaction with an environment. This interaction perturbs the system states and causes loss of information from the system to the environment. However by applying quantum feedback control, the system can “fight” against this loss of information. The main obstacle is that standard strategies from classical control are not immediately applicable to quantum systems. While there has been much development on the theoretical side, there remain key open questions concerning optimality, robustness, and best design methods for dealing with generic quantum models which can be implemented in concrete experiments with less difficulties. The first objective of Q-COAST is to develop more efficient and robust strategies for quantum feedback design applied to open quantum systems. As a second objective, we investigate the situation where the inputs are in non-classical states, the case where the generalization from the classical to the quantum case becomes more difficult. Such states are critically important for scalable quantum information processing. Our third objective is to go beyond the existing tools to design estimators and controllers. This will be achieved by introducing new pathways through the interaction between fields of quantum statistical mechanics, quantum information geometry, quantum filtering, and quantum feedback control. The final goal is to develop further numerical simulations of quantum components as well as implementing our proposed strategies in real experiments. The experimental implementations can be realized as the project will involve collaboration with leading experimental groups who have been successfully applying feedback control theoretic principles to actual quantum systems.

The main goal of TraDE-Opt is the education of 15 experts in optimization for data science, with a solid multidisciplinary background, able to advance the state-of-the-art. This field is fast-developing and its reach on our life is growing both in pervasiveness and impact. The central task in data science is to extract meaningful information from huge amounts of collected observations. Optimization appears as the cornerstone of most of the theoretical and algorithmic methods employed in this area. Indeed, recent results in optimization, but also in related areas such as functional analysis, machine learning, statistics, linear algebra, signal processing, systems and control theory, graph theory, data mining, etc. already provide powerful tools for exploring the mathematical properties of the proposed models and devising effective algorithms. Despite these advances, the nature of the data to be analyzed, that are “big”, heterogeneous, uncertain, or partially observed, still poses challenges and opportunities to modern optimization. The key aspect of the TraDE-Opt research is the exploitation of structure, in the data, in the model, or in the computational platform, to derive new and more efficient algorithms with guarantees on their computational performance, based on decomposition and incremental/stochastic strategies, allowing parallel and distributed implementations. Advances in these directions will determine impressive scalability benefits to the class of the considered optimization methods, that will allow the solution of real world problems. To achieve this goal, we will offer an innovative training program, giving a solid technical background combined with employability skills: management, fund raising, communication, and career planning skills. Integrated training of the fellows takes place at the host institute and by secondments, workshops, and schools. As a result, TraDE-Opt fellows will be prepared for outstanding careers in academia or industry.

During the past few years many projects and initiatives were undertaken deploying and testing Automated Vehicles (AVs) for public transportation and logistics. However in spite of their ambition, all of these projects stayed on the level of elaborated experimentation and never reached the level of a large-scale commercial deployment of transport services. The reasons for this are many, the most important being the lack of economically viable and commercially realistic models, the lack of scalability of the business and operating models, and the lack of user oriented services required for large end-user adoption of the solutions. The ULTIMO project will create the very first economically feasible and sustainable integration of AVs for MaaS public transportation and LaaS urban goods transportation. ULTIMO aims to deploy in three sites in Europe 15 or more multi-vendor SAE L4 AVs per site. A user centric holistic approach, applied throughout the project, will ensure that all elements in a cross-sector business environment are incorporated to deliver large-scale on-demand, door-to-door, well-accepted, shared, seamless-integrated and economically viable CCAM services. We target the operation without safety driver on-board, in a fully automated and mission management mode with the support of innovative user centric passenger services. ULTIMO’s innovative transportation models are designed for a long-term sustainable impact on automated transportation in Europe, around the globe and on society. The composition of the consortium ensures the interoperability between multiple stakeholders by making adoption of new technology at minimum costs and maximum safety. The integration of the ongoing experiments of previous AV-demonstrator projects ensures highest possible technical and societal impacts from the very beginning of the project, as well as during the project lifetime and even long after its completion.

Solid Oxide Fuel/Electrolysis Cells are electrochemical devices based on ceramics which operate at high temperature, typically 600-800 °C. This high temperature is needed to ensure fast diffusion and reaction rates i.e. to allow for high power efficiency. Unfortunately, coupled with extreme operating conditions, high working temperatures lead to fast degradation. Materials discovery efforts have thus targeted new electrolyte and electrode materials with improved ionic and/or electronic conductivity and electrochemical activity, able to operate at a lower temperature. Other strategies concerned the development of new types of solid oxide cells, based on new charge carriers. Among these, Proton Conducting Cells, which can operate at a temperature below 600°C, are particularly promising. With typical performances of 0.3 W/cm2 at 600 °C in 2013, they can now reach 1.3 W/cm2 at 600 °C as reported in 2018. This is an increase of more than 300% in five years, which represents a significant acceleration. To achieve such a performance, materials have been designed with complex compositions having typically 4-5 different cations, whose relative ratios were determined empirically. Still, the exploration of new or optimized compositions remains limited by the highly time-consuming tasks to fully characterize such materials. Thus, in the highly competitive international context of cells development and fabrication, new approaches allowing a fast screening of many compositions might constitute an efficient strategy to fasten the development of high-performing cells. The objective of AutoMat-ProCells project is precisely to combine advanced research tools for screening efficiently the intrinsic properties of oxide materials for proton-conducting oxide cells. It is based on a high-throughput experimental approach. More concretely, our project couples the development of combinatorial deposition for the preparation of materials library bu pulse laser deposition, their exhaustive structural/chemical characterization in a highly efficient way including synchrotron-based techniques, and the measurement of electrolyte/electrode properties through electrical, isotope exchange and nuclear probe measurements. From this, we will obtain unique information on structure, stability, hydration, conductivity, electrochemical activity, the kinetics of ionic species transfer and diffusion, this for an extensive range of compositions. Through AutoMat-ProCells, we will also pave the path toward a renewed strategy for a very efficient exploration of materials for SOCs. From AUTOMAT-PROCELLS, we expect the following results: - a validation of the High-Throughput approach for the study and discovery of materials for PCFCs/PCECs, including the characterization of hydration and transport properties, stability and structural-chemical features, - the production of exhaustive information (hundreds of different compositions tested) on important phase diagrams for proton-conducting solid oxide cells : BaZr0.8Y/Yb0.2O3-d- BaCe0.8Y/Yb0.2O3-d- BaSn0.8Y/Yb0.2O3-d ; LSM-LSC-LSF, or doped BaCo0.4Fe0.4Zr0.2FeO3-d, - the identification of original compositions with optimized exchange, transport and electrochemical properties for proton-conducting solid oxide cells, - the creation of technical advances in the field of High-throughput Experiments for materials discovery like (i) the design and fabrication of a furnace for large samples particularly adapted to the characterization of materials library (ii) the development of a low-cost route for combinatorial deposition of oxide materials (see below) (iii) the adaptation of SIMS for the characterization of combinatorial films. - to help for the emergence of a dynamic in the French materials science community (starting from the application on fuel cells) toward the use of automated and parallelized approaches in research.

5G networks are expected to revolution our living environments, our cities and our industry by connecting everything. 5G design has, thus, to meet the requirements of two “new” mobile services: massive Machine-Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC). Slicing concept facilitates serving these services with very heterogeneous requirements on a unique infrastructure. Indeed, slicing allows logically-isolated network partitioning with a slice representing a unit of programmable resources such as networking, computation and storage. Slicing was originally proposed for core networks, but is now being discussed for the Radio Access Network (RAN) owing to the evolution of technologies which now enable its implementation. These technologies include mainly the tendency for virtualizing the RAN equipment and its programmable control, the advent of Mobile Edge Computing (MEC) and the flexible design of 5G on the physical and MAC layers. However, the complete implementation of slicing in the RAN faces several challenges, in particular to manage the slices and associated control and data planes and for scheduling and resources allocation mechanisms. MAESTRO-5G project develops enablers for implementing and managing slices in the 5G radio access network, not only for the purpose of serving heterogeneous services, but also for dynamic sharing of infrastructure between operators. For this aim the project puts together exerts on performance evaluation, queuing theory, network economy, game theory and operations research. MAESTRO-5G is expected to provide: •A resource allocation framework for slices, integrating heterogeneous QoS requirements and spanning on multiple resources including radio, backhauling/fronthauling and processing resources in the RAN. •A complete slice management architecture including provisioning and re-optimization modules and their integration with NFV and SDN strata. •A business layer for slicing in 5G, enabling win-win situations between players from the telecommunications industry and the verticals, ensuring that the 5G services are commercially viable and gain acceptance in the market. •A demonstrator showing the practical feasibility as well as integration of the major functions and mechanisms proposed by the project, on a 5G Cloud RAN platform. The enhanced platform is expected to support the different 5G services (eMBB and IoT) and to demonstrate key aspects of slicing, such as: - Ability to create and operate in parallel multiple slices, on the same infrastructure and sharing the same radio resources (e.g. spectrum), each having different service requirements. - Ability to create and operate in parallel and independently different slices, sharing the same infrastructure/spectrum, belonging to different business actors, such as different operators. - Demonstrate inter-slice control ensuring respect of SLAs and a fair resource sharing.