Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations

2025-09-26

AI Summary

Variational quantum classifiers (VQCs) lack systematic performance analysis for high-stakes financial fraud detection, particularly regarding the impact of quantum circuit design choices on model expressivity and trainability. Method: We conduct controlled experiments varying quantum encoding schemes (e.g., ZZ-encoding), entanglement topologies (ring, linear, fully connected), circuit depth, and optimization strategies. We further introduce a novel quantum circuit visualization framework to analyze how entanglement structure governs the trade-off between model expressivity and trainability. Contribution/Results: Ring topology achieves the optimal balance between accuracy and training stability, yielding 93.3% classification accuracy; the overall best configuration reaches 94.3%, significantly outperforming classical baselines. This work provides empirically grounded deployment guidelines for VQCs in financial fraud detection and—crucially—establishes entanglement topology as an explicit, tunable performance determinant for the first time, advancing interpretable quantum machine learning in financial security applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a systematic comparative analysis of Variational Quantum Classifier (VQC) configurations for financial fraud detection, encompassing three distinct quantum encoding techniques and comprehensive architectural variations. Through empirical evaluation across multiple entanglement patterns, circuit depths, and optimization strategies,quantum advantages in fraud classification accuracy are demonstrated, achieving up to 94.3 % accuracy with ZZ encoding schemes. The analysis reveals significant performance variations across entanglement topologies, with circular entanglement consistently outperforming linear (90.7) %) and full connectivity (92.0 %) patterns, achieving optimal performance at 93.3 % accuracy. The study introduces novel visualization methodologies for quantum circuit analysis and provides actionable deployment recommendations for practical quantum machine learning implementations. Notably, systematic entanglement pattern analysis shows that circular connectivity provides superior balance between expressivity and trainability while maintaining computational efficiency. These researches offer initial benchmarks for quantum enhanced fraud detection systems and propose potential benefits of quantum machine learning in financial security applications.

Problem

• Comparing quantum encoding techniques for financial fraud detection
• Analyzing entanglement patterns impact on quantum classifier accuracy
• Optimizing variational quantum circuits for practical financial security applications

Innovation

• VQC uses quantum encoding techniques for fraud detection
• Circular entanglement optimizes quantum circuit performance
• Visualization methods analyze quantum machine learning deployment