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Paper 1

Quantum-Enhanced Graph Analytics: A Hybrid AI Framework for Seller Fraud Detection in Online Marketplaces

Postdoctoral Fellow, Center for Quantum Neural Networks, Harvard University, Laura Thompson

Year

2023

Journal

Stem Cell, Artificial Intelligence and Data Science Journal

DOI

10.64206/99ssyx46

arXiv

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Fraudulent seller networks in e-commerce platforms exploit relational patterns across buyers, products, and transactions to perpetrate large-scale scams that evade traditional detection systems. Graph Neural Networks (GNNs) provide end-to-end representation learning on graph structures, enabling detection of anomalous subgraphs indicative of fraud rings. Complementing GNNs, TinyML brings on-device inference for continuous, low-latency edge monitoring, and emerging Quantum Neural Networks (QNNs) promise enriched feature spaces for small-data regimes. This article delivers an expanded, scholarly framework covering: (1) formalization of fraudulent seller detection as a graph anomaly-ranking problem; (2) data pipelines and graph construction best practices; (3) detailed GNN architectures (GCN, GAT, GraphSAGE, graph autoencoders) and hybrid classifiers; (4) integration of TinyML for edge deployments; (5) incorporation of QNN modules for anomaly scoring; (6) comprehensive experimental evaluation on real and synthetic datasets; and (7) ethical, security, and regulatory considerations. We conclude with a multi-horizon research roadmap from near-term pilots to long-term fault-tolerant quantum defenses.

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Paper 2

A Modular Software Stack for Quantum Computing From Born-Rule Equilibrium to Nonequilibrium-Aware Quantum Software

Balfagón C.

Year

2026

Journal

Europe PMC

DOI

10.21203/rs.3.rs-8492293/v1

arXiv

-

No abstract.

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