Quantum Machine Learning for Financial Data Streams

** 机遇：** 金融机构需要持续解读复杂的数据流，以提取必要的信息，从而在绿色金融背景下提供准确的信用风险评估、管理做市服务以及预测排放。当前用于帮助和提供这些服务的经典机器学习（ML）技术具有局限性，因为这些数据流在复杂性方面不断发展。金融机构在努力改善其服务时，正在寻求解决三个关键挑战：（1）为客户提供准确的信用风险评估服务，（2）为做市服务提供有竞争力的费率，以及（3）预测排放量，以便根据ESG目标做出明智的可持续财务决策。改进当前的经典ML方法可以降低风险，提高市场利率，并为金融机构及其客户提供有针对性的可持续投资。最近的量子计算进展有可能为金融机构所依赖的计算提供重大改进，以提高效率，降低风险，为客户提供更好的服务并开发个性化产品。该团队使用尖端的量子机器学习技术，在优化的全栈Rigetti平台上运行，将为金融机构提供垂直集成的解决方案，使他们能够使用NISQ时代量子计算的全部功能。我们将开发量子签名内核，并利用这些结果来增强Rigetti最近在量子内核方面的突破。我们将针对流数据的经典ML方法对结果进行基准测试。此外，我们将构建和研究量子算法，用于计算长和高维数据流的有效签名及其内积。** 创新和效益：** 成功的项目成果将为英国金融部门和量子计算行业带来重大利益，包括参与组织。加快金融数据流量子机器学习的发展，将使渣打银行成为未来量子经济的行业领导者，并继续为客户提供最佳服务。开发量子解决方案也将支持英国金融业。Rigetti将能够加速其工作，以实现窄量子优势，即量子计算机优于最佳经典资源的点。该项目还将为伦敦帝国理工学院提供测试新量子机器学习工具的框架和用例。开放这些工具将进一步允许英国学者为自己的应用测试最先进的量子算法（可能超出本提案的范围）。

**The Opportunity:** Financial institutions need to continuously interpret complex data streams to extract information necessary for providing accurate credit risk evaluation, managing market-making services, and predicting emissions in the context of green finance. Current classical machine learning (ML) techniques used to assist and provide insights to these services have limitations as these data streams evolve in complexity. There are three key challenges that financial institutions are seeking to address in an effort to improve their offerings: (1) Providing clients accurate credit-risk evaluation services, (2) Offering competitive rates for market-making services, and (3) Predicting emissions for informed sustainable finance decisions in line with ESG targets. Improving upon the current classical ML approaches could result in reduced risk, better market rates and targeted sustainable investments for financial institutions and their customers.**The Approach:** Recent quantum computing advances have the potential to offer significant improvements to the computations financial institutions rely on to improve upon efficiency, to reduce risk, to provide better service to customers and to develop personalised products. The team's offering using cutting-edge quantum machine learning techniques, running on an optimised full-stack Rigetti platform, will offer financial institutions a vertically integrated solution, allowing them to use the full capability of NISQ-era quantum computing. We will develop quantum signature kernels and leverage the results to enhance Rigetti's recent breakthroughs in quantum kernels. We will benchmark the results against classical ML methods for streamed data. Additionally, we will build and study quantum algorithms for computing efficient signatures and their inner products for long and high-dimensional data streams. **Innovation and Benefits:** A successful project outcome will have significant benefits for the UK financial sector and the quantum computing industry, including the participating organisations. Accelerating the development of quantum machine learning for financial data streams will enable Standard Chartered to be an industry leader in a future quantum-ready economy and continue to provide the best possible services to its clients. Developing quantum-enabled solutions will also bolster the UK finance sector. Rigetti will be able to accelerate its work to achieve narrow quantum advantage, the point at which a quantum computer outperforms the best classical resources. The project will also benefit Imperial College London by providing a framework for and use cases to test new quantum machine learning tools. Making these tools open access will further allow UK academics to test state-of-the-art quantum algorithms for their own applications (possibly beyond those in this proposal).