We will establish a UK quantum device prototyping service, focusing on design, manufacture, test, packaging and rapid device prototyping of quantum photonic devices. QuPIC will provide academia and industry with an affordable route to quantum photonic device fabrication through commercial-grade fabrication foundries and access to supporting infrastructure. QuPIC will provide qualified design tools tailored to each foundry's fabrication processes, multiproject wafer access, test and measurement, and systems integration facilities, along with device prototyping capabilities. The aim is to enable greater capability amongst quantum technology orientated users by allowing adopters of quantum photonic technologies to realise advanced integrated quantum photonic devices, and to do so without requiring in-depth knowledge. We will bring together an experienced team of engineers and scientists to provide the required breadth of expertise to support and deliver this service. Four work packages deliver the QuPIC service. They are: WP1 - Design tools for photonic simulation and design software, thermal and mechanical design packages and modelling WP2 - Wafer fabrication - Establishing the qualified component library for the different fabrication processes and materials and offering users a multi-project wafer service WP3 - Integrated device test and measurement - Automated wafer scale electrical and optical characterisation, alignment systems, cryogenic systems to support single-photon detector integration) WP4 - Packaging and prototyping - Tools for subsystem integration into hybrid and functionalised quantum photonic systems and the rapid prototyping of novel, candidate component designs before wafer-scale manufacturing and testing The design tools (WP1) will provide all the core functionality and component libraries to allow users to design quantum circuits, for a range of applications. We will work closely with fabrication foundries (WP2) to qualify the design libraries and to provide affordable access to high-quality devices via a multi-project wafer approach, where many users share the fabrications costs. Specialist test and measurement facilities (WP3) will provide rapid device characterization (at the wafer level), whilst packaging and prototyping tools (WP4) will allow the assembly of subsystems into highly functionalised quantum photonic systems.

Modern society depends massively on the generation, processing and transmission of vast amounts of data. It is predicted that by 2025, 175 zettabytes (175 trillion gigabytes) of data will be generated around the globe, with so-called 'edge computing' devices creating more than 90 zettabytes alone. Processing such huge amounts of data demands ever increasing computational power, memory and communication bandwidth - demands that cannot be sustainably met by conventional digital electronic technologies. The growing gap between the needs and the capabilities of today's information technology is exemplified if we consider the historical trend in total number of computations (in units of #days of calculating at a rate of 1 PetaFLOP/s) needed to train various artificial intelligence (AI) systems. The trend followed Moore's Law (doubling approximately every two years) until 2012, after which the doubling time reduced to a mere 3.4 months! This trend is compounded by the breakdown in Koomey's Law, which states that the number of computations per Joule of energy doubles around every 1.5 years. This law was also followed until quite recently, but we are now approaching a widely accepted computing efficiency-wall at around 10 GMAC/Joule (a MAC is a multiply-accumulate operation) for CMOS electronics and the von-Neumann architecture. As a result, the energy consumption used in training modern AI systems is truly staggering, with consequent adverse effects for sustainability. This has led to a move away from standard CPU designs in AI towards the use of co-processors - GPUs, ASICs, FPGAs - with superior parallelism. However, even here the limitations of electrical signalling lead to massive levels of energy consumption. It was recently estimated, for example, that the training of a large GPU-based natural language processing system used for accurate machine translation resulted in carbon dioxide emissions equivalent to lifetime use of 5 cars! Clearly, a new approach is needed. Thus, in the APT-NuCOM project we will develop a highly efficient novel non-von Neumann co-processor that exploits clear advantages offered by photonic computation, but at the same time links seamlessly with the electronic domain to enable integration with existing electronic computing infrastructure. The APT-NuCOM co-processor will exploit novel phase-change photonic in-memory computing concepts to deliver massively parallel computation at PetaMAC/s speeds and, ultimately, an energy budget approaching that of the human brain.