10072633Quantum Unsupervised Learning for Anti-Money Laundering DetectionClosedFeasibility StudiesInnovate UK**The Opportunity:** Financial crime is one of the fastest-growing areas of risk management. Money laundering in particular poses a significant threat to national and international security. Inadequate methods for detecting financial crimes has serious consequences for financial organisations and society. Developing improved methods of detecting and reducing money laundering is seen as imperative by financial services institutions and their regulators. **The Approach:** Quantum algorithms for anomaly detection on classical data are rather understudied. This project will enhance current quantum machine learning models beyond supervised learning to detect anomalous behaviour indicating money laundering activity. In this work, we will use a quantum computer's output as a component to a classical machine learning algorithm by leveraging techniques that generalise projected quantum kernel methods \[1\]. The project will utilise Rigetti's quantum computer, with its scalable superconducting chip architecture, supporting rapid, high fidelity entangling gates. Rigetti has recently unlocked the possibility of executing projected quantum kernels and their generalisations at speed. **Innovation and Benefits:** Machine learning technology holds great promise to detect and prevent financial crime by flagging potential suspicious transactions and adapting to ever-changing criminal behaviour. Quantum computing can serve as an accelerator to classical computing. By extending capabilities from existing classical machine learning techniques to quantum computing enhanced capabilities, financial institutions will be better equipped to tackle financial crime. Developing improved machine learning capabilities to combat financial crime will provide financial institutions a competitive advantage, accelerate the commercialisation of quantum computing, and provide more effective ways of identifying and reducing criminal activity. By joining HSBC's deep domain expertise, the University of Edinburgh's algorithm expertise, Rigetti's state-of-the-art quantum computing hardware and software, and the National Quantum Computing Centre's pivotal role in the UK's quantum computing ecosystem, this project will not only harness industry-leading resources and world-class researchers, but will pave the way for future quantum algorithm and application development to benefit the financial services industry and numerous other verticals.\[1\] Huang et al., "Power of data in quantum machine learning," February 2021, [https://arxiv.org/abs/2011.01938][0]. [0]: https://arxiv.org/abs/2011.01938