ADOPT-TEM: Anomaly Detection OPTimisation with Tensor Error Mitigation

Sector: Finance

Lead Organisation: Algorithmiq UK Limited 

Project Partners: Rigetti UK Limited, NQCC

Observers: Financial Conduct Authority

Anomaly Detection OPTimisation with Tensor Error Mitigation (ADOPT-TEM) project focused on developing a bespoke adaptation of a leading error mitigation method, Algorithmiq TEM on a Rigetti quantum computer. The key objective of the project was to improve the hardware performance of a hybrid quantum-classical fraud detection method developed by Rigetti. Such improvements in accuracy offers a strong motivation for quantum computing adoption in the banking and digital payment industries. The work from this project is expected to accelerate the deployment of a quantum-enhanced digital payment fraud detection system in the digital economy. ADOPT-TEM provided an opportunity to test and demonstrate efficient integration of quantum hardware and software, and their extension to a hybrid quantum + HPC workflow on NQCC infrastructure.