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

Fouad Mohammed Abbou, Mohamed Bouhadda, Lamiae Bouanane, Mouna Kettani, Farid Abdi, Abdelouahab Abid

TL;DR

This work systematically evaluates Variational Quantum Classifiers for financial fraud detection by comparing three quantum encodings (ZZ, Angle, Amplitude) across three entanglement topologies (Linear, Circular, Full) on a real-world credit card transaction dataset. Using a hybrid quantum-classical optimization framework, the study finds that ZZ encoding coupled with circular entanglement delivers the strongest performance, achieving up to 94.3% accuracy and robust training dynamics, while linear and full entanglement present notable drawbacks in recall or optimization stability. The analysis provides actionable deployment guidance and a reproducible methodology for benchmarking quantum architectures on fraud detection tasks, highlighting the potential and practical considerations for near-term quantum devices. These insights contribute to understanding how encoding choices and entanglement patterns influence quantum advantage in financial security applications and lay groundwork for quantum-native deployment strategies.

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.

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

TL;DR

This work systematically evaluates Variational Quantum Classifiers for financial fraud detection by comparing three quantum encodings (ZZ, Angle, Amplitude) across three entanglement topologies (Linear, Circular, Full) on a real-world credit card transaction dataset. Using a hybrid quantum-classical optimization framework, the study finds that ZZ encoding coupled with circular entanglement delivers the strongest performance, achieving up to 94.3% accuracy and robust training dynamics, while linear and full entanglement present notable drawbacks in recall or optimization stability. The analysis provides actionable deployment guidance and a reproducible methodology for benchmarking quantum architectures on fraud detection tasks, highlighting the potential and practical considerations for near-term quantum devices. These insights contribute to understanding how encoding choices and entanglement patterns influence quantum advantage in financial security applications and lay groundwork for quantum-native deployment strategies.

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.

Paper Structure

This paper contains 19 sections, 10 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Quantum Mathematical Model
  3. Problem Definition and Objective
  4. Classical Preprocessing and Feature Selection
  5. Feature Selection via Random Forest:
  6. Data Encoding and Quantum Feature Maps
  7. ZZ Feature Map:
  8. Angle Encoding (Pauli Feature Map) :
  9. Amplitude Encoding :
  10. Entanglement Topology
  11. Linear Entanglement
  12. Circular Entanglement
  13. Full Entanglement
  14. Quantum Circuit Architecture
  15. Measurement:
  16. ...and 4 more sections

Figures (9)

  • Figure 1: Linear Entanglement.
  • Figure 2: Circular Entanglement
  • Figure 3: Full Entanglement
  • Figure 4: Entanglement Pattern Impact Analysis
  • Figure 5: Entanglement Configuration Performance Comparison.
  • ...and 4 more figures