A Novel Edge-Assisted Quantum-Classical Hybrid Framework for Crime Pattern Learning and Classification

2026-04-08

AI Summary

This study addresses the challenges posed by urbanization-induced high-dimensional and imbalanced crime data, which hinder accurate prediction and efficient police deployment using conventional methods. To overcome these limitations, the authors propose an edge-assisted quantum-classical hybrid framework that innovatively integrates domain-specific feature relationships through a correlation-aware quantum circuit. The approach synergistically combines quantum algorithms—such as the Quantum Approximate Optimization Algorithm (QAOA)—with classical machine learning techniques. Evaluated on 16 years of crime data from Bangladesh, the proposed architecture achieves an accuracy of 84.6% with minimal parameters and low computational overhead, significantly outperforming purely classical models. These results demonstrate the framework’s efficiency and practical viability for deployment in resource-constrained edge environments.

Abstract

Crime pattern analysis is critical for law enforcement and predictive policing, yet the surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets that challenge traditional classification methods. This study presents a quantum-classical comparison framework for crime analytics, evaluating four computational paradigms: quantum models, classical baseline machine learning models, and two hybrid quantum-classical architectures. Using 16-year Bangladesh crime statistics, we systematically assess classification performance and computational efficiency under rigorous cross-validation methods. Experimental results show that quantum-inspired approaches, particularly QAOA, achieve up to 84.6% accuracy, while requiring fewer trainable parameters than classical baselines, suggesting practical advantages for memory-constrained edge deployment. The proposed correlation-aware circuit design demonstrates the potential of incorporating domain-specific feature relationships into quantum models. Furthermore, hybrid approaches exhibit competitive training efficiency, making them suitable candidates for resource-constrained environments. The framework's low computational overhead and compact parameter footprint suggest potential advantages for wireless sensor network deployments in smart city surveillance systems, where distributed nodes perform localized crime analytics with minimal communication costs. Our findings provide a preliminary empirical assessment of quantum-enhanced machine learning for structured crime data and motivate further investigation with larger datasets and realistic quantum hardware considerations.

Problem

• crime pattern classification
• high-dimensional imbalanced data
• edge computing
• quantum-classical hybrid
• resource-constrained environments

Innovation

• quantum-classical hybrid
• edge-assisted computing
• QAOA
• correlation-aware circuit
• crime pattern classification