Abstract: Spectral Graph Compression For Practical Quantum Deployment In Financial Recommender Systems: The Final Step Toward A Tier-1 Implementation
This paper presents the third and final study in a research series, initially published in MDPI Computers, that directly addresses the path from theoretical quantum algorithm analysis to practical deployment at a Tier-1 financial institution. While previous papers established the quan- tum readiness of recommender algorithms and introduced initial compression techniques, this final study solves the critical deployment bottleneck: creating a compression pipeline that can recover the original high-dimensional customer data while providing recommendations consistent with customer experience. The fundamental complexity stems from financial data dimensionality—where customer vectors typically contain 50+ fields (versus 3 to 5 in e-commerce)—creating an exponentially larger solution space that makes the clustering problem substantially more challenging than conventional recommendation tasks. Building upon the foundational work in [1, 2], we introduce an enhanced spectral graph compression method that strategically expands the problem to 1024 nodes before com-
pressing to hardware-feasible dimensions. Crucially, our pipeline addresses the sampling bottleneck identified in earlier work, achieving 98% in-sample and ∼95% out-of-sample accuracy via leave-one- out cross-validation, while meeting the bank’s requirement for data recovery and recommendation consistency. This work completes the trilogy of studies needed to translate quantum advantage into operational financial systems.
Keywords: quantum computing, QAOA, financial recommender systems, spectral graph com- pression, high-dimensional clustering, quantum finance deployment
