10073463Quantum Optical Neural Networks for Quench PreventionClosedFeasibility StudiesInnovate UKThe need for secure, clean, reliable, and sustainable sources of energy has grown in both importance and urgency. Part of the solution to meet these needs is nuclear fusion. While experimental progress in fusion has evidenced its viability, a range of engineering challenges must be met and coordinated before fusion reactors can operate reliably for long periods, and to deliver a net energy gain. Among these challenges is the processing of large real-time data sets from cryogenically cooled superconducting magnetic coils that maintain the plasma from which energy is released. Superconductivity can break down if a hotspot forms in part of a coil; the subsequent rapid warming and loss of plasma confinement results in damage and downtime. To prevent this, hotspots must be rapidly located so individual coils can be protected. Hotspots can be detected using a process called optical frequency domain reflectometry (OFDR). Laser light is sent down an optical fibre that is co-wound with a coil; a hotspot affects some of the light reflected back along the fibre; its detection allows the hotspots to be located. However, precisely locating hotspots in multiple coils within fractions of a second, requires the rapid processing of vast amounts of data. This information processing challenge is a barrier to clean energy from fusion. As information processing has matured beyond the central processing unit (CPU), a variety of tailored control and computational hardware has emerged including graphics processing units (GPUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), Neural Networks (NNs) and quantum computing. Each of these sacrifices a general purpose (classical) computing capability to enable much greater power for particular information processing tasks. The people at Duality Quantum Photonics have pioneered integrated photonics as a platform for both Optical Neural Nets (ONNs) and quantum information processing. Quantum Optical Neural Nets (QONNs), the combination of these two paradigms, in integrated photonics, provide an appealing platform for a range of information processing tasks, including the processing of real-time data required to sustain fusion energy generation. In this project, Duality will partner with the private fusion energy company Tokamak Energy, and with the UK Atomic Energy Authority, to design and fabricate QONNs in photonic chips to process OFDR data for the rapid location of hotspots. The project will demonstrate how quantum computing can help tackle some of the information processing challenges that stand in the way of net gain fusion energy.