Rigetti & Algorithmiq Partner to Tackle Financial Fraud with Quantum Machine Learning

Rigetti and Algorithmiq are partnering to combat financial fraud using quantum machine learning, announced today, February 20, 2026. The collaboration focuses on anomaly detection, a critical technique for preventing fraudulent transactions, and aims to improve the performance of hybrid quantum-classical methods for digital payment systems. A key component of this project involves implementing a novel adaptation of Algorithmiq’s Tensor-network Error Mitigation on Rigetti’s 36-qubit quantum computer hosted at the UK’s National Quantum Computing Centre (NQCC). “We look forward to pushing the boundaries of what’s possible with today’s current quantum hardware as we deepen our understanding of integrating advanced error mitigation solutions with our quantum computers,” Rigetti stated. This work is supported by a newly awarded proof-of-concept project from the 2025 STFC Cross Cluster Proof of Concept: SparQ Quantum Computing Call.

Rigetti and Algorithmiq Advance Hybrid Quantum Anomaly Detection

Quantum machine learning is now being directly applied to financial data by Rigetti, with a specific focus on bolstering fraud prevention techniques. The company is collaborating with Algorithmiq to refine hybrid quantum-classical anomaly detection methods, aiming to enhance digital payment systems and accelerate their adoption within the broader digital economy. The collaboration represents a significant step toward realizing practical applications of quantum computing in the financial sector.

Tensor-Network Error Mitigation on 36-Qubit NQCC Computer

Rigetti is currently concentrating research efforts on applying quantum machine learning techniques to analyze genuine financial datasets. A key component of this work involves a collaboration with Algorithmiq to refine hybrid quantum-classical methods for anomaly detection, a crucial tool in preventing financial fraud. The companies are specifically targeting improvements to a fraud detection system and aiming to speed up the introduction of quantum-enhanced digital payment solutions, leveraging a novel implementation of Algorithmiq’s Tensor-network Error Mitigation. The integration of this advanced error mitigation is intended to maximize the capabilities of existing quantum hardware, allowing for a deeper understanding of how to combine these solutions with Rigetti’s computers.