Quantum photonic neural networks to predict instabilities in tokamaks

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Achieving Net Zero emissions is a critical and necessary step towards mitigating climate change and an essential component of a comprehensive strategy to limit global warming; meanwhile, the need for secure and reliable sources of energy has grown in importance. Nuclear fusion has the potential to be carbon-neutral and produce sustainable energy without significant greenhouse gas emissions. But although experimental progress increasingly evidences the viability of fusion, a range of engineering challenges must be met before fusion reactors can operate reliably for long periods, to deliver a net energy gain. Among these challenges is preventing disruptions of the plasma from which energy is released. In addition to reducing efficiency, disruption events can damage fusion reactors and cause significant power plant downtime. However the behaviour of a fusion plasma is complex and large data sets generated from diagnostics must be rapidly processed, making it very challenging to predict disruption events in sufficient time to allow mitigating action. Specialised computational hardware can be tailored to implement machine learning and provide real time data analysis. Popular special purpose processors include graphics processing units (GPUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and Optical Neural Nets (ONNs). Each of these sacrifices a general purpose computing capability to enable much greater power for particular information processing tasks. More recently, quantum technologies have attracted attention for their ability to deliver exponential speedups for certain information processing tasks. As a platform for supporting both quantum information processing and machine learning, integrated photonics is versatile, robust, and manufacturable. In this project Duality Quantum Photonics (DQP) will develop Quantum Photonic Neural Nets (QPNNs) to rapidly analyse the large data sets from plasma diagnostics and predict disruption events. DQP will design and fabricate QPNNs in chips using new materials that are best suited to photonic quantum information processing and are resilient to radiation and large magnetic fields. DQP's QPNNs will be developed with advice from experts within the UK Atomic Energy Agency. QPNNs can ultimately be produced at scale so that large numbers of fusion reactor components can be individually monitored and controlled in real time. DQP have brought together several cutting edge approaches to photonic chip manufacture, quantum information processing and AI. Bringing this powerful set of technologies to bear on the grand engineering challenge of delivering net positive fusion energy opens an exciting new era of science and technology, and clean sustainable energy for all our futures.