Quantum Machine Learning for Financial Data Streams

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**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).