Quantum-Enhanced Prototype Retrieval: Exploring QML for Complex, High-Dimensional Case Libraries

Fatunmbi (2024) highlights how QML can outperform classical machine learning in high-dimensional, complex domains like fintech fraud detection. However, CBDS prototype retrieval still relies on classical models that may falter as case libraries grow in size and complexity (Shved et al., 2024). This idea proposes applying quantum support vector machines or quantum nearest-neighbor algorithms to the retrieval of relevant prototypes from massive, multi-attribute case libraries. Not only could QML offer speed and accuracy improvements, but quantum data encoding (e.g., QRAM) could allow entirely new ways of representing case similarities and uncertainties. This approach fundamentally challenges the computational bottlenecks of current CBDS, and if successful, could enable real-time, robust retrieval in domains previously considered intractable with classical methods. It’s a bold cross-disciplinary leap with the potential to redefine computational boundaries in CBDS.

References:

1. INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH. A. Shved, Ye. O. Davydenko, H. V. Horban (2024). Radio Electronics, Computer Science, Control.
2. Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems. Temitope Oluwatosin Fatunmbi (2024). World Journal of Advanced Engineering Technology and Sciences.