In a groundbreaking collaboration, Rigetti UK Limited, a subsidiary of Rigetti Computing, has been awarded an Innovate UK grant to enhance quantum machine learning methods for anti-money laundering (AML) detection. This project brings together Rigetti, HSBC, the Quantum Software Lab (QSL) at the University of Edinburgh, and the National Quantum Computing Centre (NQCC) to tackle the growing threat of money laundering in the financial industry.
Money laundering is a significant concern for financial institutions and society as a whole. Machine learning technology has proven effective in detecting and preventing financial crime by identifying suspicious transactions and adapting to evolving criminal behavior. However, the consortium aims to take this a step further by leveraging the power of quantum computing to enhance existing machine learning algorithms.
Quantum computing holds the potential to revolutionize the field of AML detection by enabling more advanced anomaly detection models. This collaboration will focus on extending current quantum machine learning models to identify anomalous behavior indicative of money laundering activities. By harnessing the unique properties of quantum systems, the consortium aims to significantly improve the performance of AML methods compared to current state-of-the-art solutions.
The goal of Rigetti and its partners is to achieve narrow quantum advantage (nQA), a point where a quantum computer can outperform classical solutions in solving practical and operationally relevant problems. Dr. Subodh Kulkarni, Rigetti’s CEO, emphasizes the importance of nQA for the quantum computing industry and believes that addressing real-world problems like money laundering could not only provide a competitive advantage but also accelerate the development of hardware and software capabilities.
The consortium will not only leverage Rigetti’s quantum processors and software but also benefit from HSBC’s extensive domain knowledge, benchmarks, and expertise in classical and quantum machine learning. The University of Edinburgh’s quantum algorithm expertise and the NQCC’s quantum computing opportunities and resources will further strengthen the consortium’s efforts. Beyond improving AML methods, the collaboration aims to bolster the UK’s quantum ecosystem and establish the country as a global leader in the quantum computing sector.
HSBC, which already employs machine learning techniques to detect anomalous customer behavior, recognizes the potential of quantum computing to enhance its services and mitigate risks. By collaborating directly with academia and quantum computing vendors, HSBC aims to test and implement quantum-enabled solutions more efficiently.
The Quantum Software Lab (QSL) at the University of Edinburgh, launched in partnership with the NQCC, plays a pivotal role in this collaboration. The QSL serves as a platform to bridge the gap between quantum research and industrial integration by exploring real-world quantum computing applications. Professor Elham Kashefi, Chief Scientist at NQCC and Head of QSL, highlights the significance of this project in showcasing quantum’s potential and stimulating user adoption.
The project officially commenced on September 1, 2023, and is expected to last for 18 months. As this collaboration progresses, it holds the promise of a breakthrough in combating money laundering and advancing the field of quantum machine learning.
- What is money laundering?
- What is quantum machine learning?
- What is narrow quantum advantage (nQA)?
Money laundering is the process of disguising the origins of illegally obtained money to make it appear legitimate.
Quantum machine learning is an emerging field that explores the intersection of quantum computing and machine learning algorithms. It aims to leverage the unique properties of quantum systems for enhanced data analysis and pattern recognition.
Narrow quantum advantage refers to the point at which a quantum computer can solve a practical problem more efficiently or effectively than classical computers, offering a clear advantage in a specific application area.