AI, Data Science, and Quantum Neural Networks inE-Commerce: Methods, Applications, Risks, and a Research Roadmap

Abstract

Ecommerce has matured into a data-intensive ecosystem where consumer behavior, logistics, pricing, personalization, and fraud detection are driven by large, heterogeneous datasets. Classical AI and data-science pipelines have delivered major productivity and
revenue gains, yet they face limits in modeling combinatorial recommendation spaces, accelerating molecular-scale cryptography for secure transactions, and solving certain optimization problems at scale. Quantum Neural Networks (QNNs) and hybrid quantum-classical approaches promise novel representational capacity and computational primitives that can enrich recommender systems, optimization for supply chains, privacy-preserving analytics, and next-generation fraud detection. This article synthesizes theory and practice across AI, data science, and QNNs for e-commerce. It offers: (1) a conceptual framework linking data modalities and business problems; (2) detailed methods for modeling, training, deployment, and evaluation; (3) extended technical sections on quantum representations, QNN architectures, and hybrid pipelines with reproducible pseudocode; (4) applied case studies (recommendation, dynamic pricing, inventory optimization, personalization, fraud detection, privacy); (5) engineering and MLOps considerations; (6) an ethics, privacy, and regulatory analysis; and (7) a prioritized research and deployment roadmap. The paper targets researchers and practitioners aiming to integrate quantum-enhanced AI into production e-commerce systems while preserving interpretability, fairness, security, and economic value.