Quantum Optical Neural Networks for Quench Prevention

用于预防猝灭的量子光神经网络

• 批准号: 10073463
• 金额: $ 45.43万
• 依托单位: DUALITY QUANTUM PHOTONICS LTD
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10073463
• 关键词: Quantum; Optical; Neural; Networks; Quench

The 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.

对安全，清洁，可靠和可持续的能源的需求在重要性和紧迫性方面增长。满足这些需求的解决方案的一部分是核融合。尽管融合的实验进展已经证明了其生存能力，但在融合反应器可以长时间可靠地运行并提供净能量增益之前，必须应对和协调一系列的工程挑战。这些挑战是通过从低温冷却的超级导向磁性磁盘中处理的大型实时数据集，以维持能源可从能源中释放出的质量。如果在线圈的一部分中形成热点，超导性可能会分解；随后的快速变暖和血浆限制的丧失会导致损坏和停机时间。为了防止这种情况，热点必须迅速定位，以便可以保护单个线圈。可以使用称为光频域反射测量法（OFDR）的过程检测到HOTSPOTS。激光光被送到与线圈共绕的光纤；热点会影响沿着纤维反射的一些光；它的检测使热点可以找到。但是，精确地将热点定位在一秒钟内的多个线圈中，需要快速处理大量数据。这种信息处理挑战是融合中清洁能源的障碍。随着信息处理已经超越了中央处理单元（CPU），已经出现了各种量身定制的控制和计算硬件，包括图形处理单元（GPU），应用程序特定的集成电路（ASICS），现场可编程栅极阵列（FPGAS）（FPGAS），NNS，NNS（NNNIN）（NNNUM）（NNNUM）。这些牺牲中的每一个都具有通用（经典的）计算能力，以使特定信息处理任务更大的功能。二元性量子光子学的人们将综合光子学率先作为光学神经网（ONNS）和量子信息处理的平台。量子光学神经网（QONNS），这两个范式的组合在集成的光子学中为一系列信息处理任务提供了一个吸引人的平台，包括处理融合能源产生所需的实时数据。在该项目中，二元性将与私人融合能源公司的tokamak Energy Wistract and Dromination和Photoct of Phototic of Pothation for Photone fraponing of Photone Quons for Pothine Quons of Drapone Qonn，以构成Qonn，以设计和构成。热点的位置。该项目将证明量子计算如何帮助应对阻碍净增益融合能的一些信息处理挑战。