AI, Data Science, and Quantum Neural Networks in E‑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.