This proposal introduces a pioneering new on-orbit services system concept which would rapidly industrialize the European space ecosystem, making Europe a world leader in robotized and sustainable modular infrastructures as well as reusable launchers, with additional competitive benefits for a sustainable European digital industry and sovereign cloud autonomy. European space technology has now reached a level of maturity that makes possible a revolutionary – yet feasible – endeavour: the installation of internet data centres in orbit, in order to reduce the exponential impact of digital technology on energy consumption and on climate warming. The installation of large modular space infrastructures with robotic assembly, megawatt level space-based solar power, high throughput optical communications, low cost and reusable launchers, is now within the European space industry’s capability. The goal of the proposed study is to demonstrate that placing future data centre capacity in orbit, using solar energy outside the earth’s atmosphere, will substantially lower the carbon footprint of digitalization. Space data centres could therefore become an active contributor to the EC Green Deal objective of carbon neutrality by 2050, which would justify the investment required to develop and install such a large space infrastructure system. It would also strengthen Europe’s digital sovereignty and autonomy, for a sustainable and prosperous digital future. Given the ambition and huge potential impact of this project, which would become a major European flagship program, a broad system-level feasibility and business study is necessary. For that purpose, the ASCEND consortium has brought together major players in the fields of environment analysis (Carbone 4, Vito), data centres architecture, hardware and software (Orange, CloudFerro, HPE), space systems development (Thales Alenia Space, Airbus, DLR), and access to space (ArianeGroup).

One of the main needs in Quantum Optics and Quantum Information is the ability to generate, manipulate and characterize arbitrary quantum states both in discrete and continuous variable domains. Q-ONE aims at exploring a novel approach for sensing and generating quantum states of light based on quantum neural networks (QNN) in integrated photonic devices. This proposal has the ambition to solve one of the most interesting problems of quantum mechanics: the recognition of quantum states of photons, like Fock states or entangled pairs, without the need of correlation measurements. Moreover, our platform has the ability to be reversible: by injecting a quantum state into the QNN, the output gives access to the full characterization of the input quantum state; conversely, with a classical state of light as input (a coherent state, emitted by a laser), an arbitrary quantum state can be generated on demand at the output of the QNN. This is all realised in a single device. The project idea places itself at the frontier between quantum physics and applied artificial intelligence, building on top of state-of-the-art semiconductor material growth and processing. The consortium targets the realization of a novel device based on strongly interacting photons (exciton-polaritons) that, using principles of neuromorphic computing, is able to recognize, characterize, and generate a variety of quantum states. Importantly, we propose to exploit the properties of a quantum neural network which is able to identify and generate quantum states without the need to reach extreme single-particle interaction strengths: this innovative idea relies on the physical realization – rather than the simulations – of a massively parallel computing task. If successful, the Q-ONE approach will enable the realization of a completely new, fully reconfigurable and reversible universal quantum platform which will significantly advance the state of the art in the field of Quantum Technologies

Rapid advances in artificial intelligence technologies have led to powerful models and algorithms that have revolutionized many applications across all fields of science and technology. Deep learning performed within artificial neural networks has yielded new ways to process data, leading to sophisticated systems with impressive functionality and benefits. However, conventional computing hardware is reaching its limits in terms of energy efficiency and speed. A new approach to computing hardware is needed. Novel brain-inspired or neuromorphic chips working with biologically-inspired spiking neural networks have gained attention as they promise highly efficient ways to process data. Important research effort has been dedicated to develop such neuromorphic systems in electronic or photonic hardware separately, each with its drawbacks and limitations. SPIKEPro proposes a science-towards-technology breakthrough by combining low-energy electrical and photonic neurons into a joint spiking neural network on an integrated circuit. SPIKEPro’s chip integration approach is based on a common technology platform, connecting ultrafast laser optical neurons with efficient electrical spiking diodes through non-volatile synaptic weights. This enables to simultaneously capitalise on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations. In addition to reducing the energy consumption per spike in the network, SPIKEPro will also develop novel learning strategies and algorithms able to work with reduced number of synaptic connections. These will be possible by exploiting the hardware parameters of the electrical and photonic spiking devices. The outcome of SPIKEPro will have lasting economic, societal and scientific impact. The project will bring ultra-fast and efficient neuromorphic hardware into the disparate fields of edge computing, sensor data processing, high-speed control and computational neuroscience.

The growing need to transfer massive amounts of data among multitudes of interconnected devices for e.g., self-driving vehicles, IoT or industry 4.0 has led to a quest towards low-power and secure approaches to locally processing data. Neuromorphic computing, a brain-inspired approach, addresses this need by radically changing the processing of information. Although neuromorphic electrical computing systems offer advantages in terms of CMOS implementations and scalability, they inherit limitations of conventional electronics such as low energy-efficiency, high latency and low bandwidth density. Besides, such systems often require robust security layers for e.g., safety-critical applications. Security layers based on memory-stored secret keys are prone to several types of memory-accessing attacks. Therefore, silicon hardware approaches for security primitives such as physical unclonable functions (PUFs) are currently investigated because of their absence of long-term digital memory storage. Although electronic PUFs have received major attention thanks to their native CMOS implementation, for secure authentication they are prone to machine learning and side-channel attacks due to their CMOS technology. The NEUROPULS project aims to build next-generation low-power and secure edge-computing systems by developing novel photonic computing architectures and security layers based on photonic PUFs in augmented silicon photonics CMOS-compatible platforms. The integration of emerging non-volatile phase change materials for synapses/neurons and III-V materials for on-chip spiking sources, for the first time, will allow to build novel neuromorphic accelerators featuring RISC-V compliant interfaces for smooth adoption and programmability. Optimal performance will be achieved thanks to a novel full-system simulation platform for design space exploration. Three relevant use-cases will be considered for benchmarking to demonstrate 2 orders of magnitude energy efficiency improvement.

The need for a next-generation computing platform becomes clear from IoT and 5G/6G and their high performance and low power requirements. Now, graphene and 2D materials (2DM) offer the unique ability to enable highly confined nonlinear interactions of light at low powers and at extremely low response times in the femtosecond range. However, it must be integrated with CMOS low-loss silicon nitride (SiN) platform that facilitates the possibility to create circuits for fast, low power, high bandwidth, general purpose computing and memory completely in the optical domain. As the most important challenge comes from the maturity of the graphene processes with standard CMOS environments, the main goal of GATEPOST is to fabricate and demonstrate a radically new graphene-based all-optical data processing platform, integrated and tested in a real CMOS pilot line. As a user case, we focus on a network security device for distributed denial of service (DDoS) detection and network packet inspection. Even though on average 170 cyber-attacks are performed per IoT device per day, there is still a huge lack of security due to the added power, latency, operating costs and bandwidth limitations involved. This is unacceptable, especially considering that the cybercrimes topped an estimated $318 billion in 2021 alone. With our graphene-based computing platform, we will show how low-power, low-latency and high bandwidth network security is ready for the IoT and 5G/6G future. The full system showcases the unique expertise of each consortium member in all-optical digital logic, neuromorphic computing, memory and ultra-fast clock generation. These components are realized in the 2D-Experimental Pilot Line at IHP, allowing for scalable fabrication and strengthening the EU’s supply-chain in high-performance computing. In the future, the developed platform can be deployed for applications in AI, autonomous driving and more, paving the way for computing beyond von Neumann and Moore’s Law.