Quantum-Assisted Flood Modeling: Pioneering Large-Scale Analysis for Enhanced Risk Assessment

该提案旨在解决使用量子计算进行大规模真实洪水建模研究的挑战。重点是利用量子计算，通过利用量子算法的力量，为此类研究指明一条道路。洪水模型包括求解浅水方程(SWE)，该方程描述了各种情况下的水流，如堤坝决口、风暴潮或河流洪水波。然而，在大范围和高分辨率上使用复杂的模型进行模拟的计算成本构成了限制。一种方法是简化模型，但这会损害准确性和有效性。该提案建议使用量子计算通过使用量子物理信息神经网络(QPINN)算法来应对这一计算挑战。该算法结合了经典数据处理和使用变分量子电路(VQC)的量子处理。数据被编码成VQC的量子门参数，随着算法的进展，这些参数被调整以提高目标函数预测的精度。该算法的可微性在噪声中尺度量子(NISQ)时代特别有价值。该项目将由欧洲著名的量子软件初创公司多重宇宙计算公司领导，合作伙伴包括牛津量子电路公司(OQC)和穆迪分析公司。多重宇宙计算将处理问题的技术公式和算法开发。量子硬件提供商OQC将提供必要的量子计算资源。全球风险评估公司穆迪分析(Moody‘s Analytics)将贡献行业专业知识、数据需求和对计算效率的见解。综上所述，这项建议寻求利用量子计算，特别是QPINN算法，来解决大规模洪水模拟研究中的计算挑战。通过利用量子算法的力量，并结合多重宇宙计算、牛津量子电路和穆迪分析的专业知识，该项目旨在为更准确、更高效的洪水风险评估和管理铺平道路。

The proposal aims to address the challenge of conducting large-scale real flood modeling studies using quantum computing. The focus is on utilising quantum computing to indicate a path towards such studies by leveraging the power of quantum algorithms. Flood modelling involves solving the Shallow Water Equations (SWE), which describe the flow of water in various scenarios such as dam breaks, storm surges, or river flood waves. However, the computational cost of running simulations with sophisticated models over large areas and high resolution poses limitations. One approach is to simplify the models, but this compromises accuracy and effectiveness. The proposal suggests employing quantum computing to tackle this computational challenge by employing a Quantum Physics-Informed Neural Network (QPINN) algorithm. The algorithm combines classical data processing with quantum processing using a Variational Quantum Circuit (VQC). The data is encoded into the quantum gate parameters of the VQC, and as the algorithm progresses, these parameters are adjusted to improve the accuracy of target function predictions. The algorithm's differentiability is particularly valuable in the Noisy Intermediate-Scale Quantum (NISQ) era. The project will be led by Multiverse Computing, a prominent quantum software start-up based in Europe, with partners Oxford Quantum Circuits (OQC) and Moody's Analytics. Multiverse Computing will handle the technical formulation of the problem and algorithm development. OQC, a quantum hardware provider, will supply the necessary quantum computing resources. Moody's Analytics, a global risk assessment firm, will contribute industry expertise, data requirements, and insights on computational efficiency. In summary, this proposal seeks to utilize quantum computing, specifically the QPINN algorithm, to address the computational challenges in large-scale flood modeling studies. By leveraging the power of quantum algorithms and combining the expertise of Multiverse Computing, Oxford Quantum Circuits, and Moody's Analytics, the project aims to pave the way for more accurate and efficient flood risk assessment and management.