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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

-

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

Fidelity-Guaranteed Entanglement Routing with Distributed Purification Planning

Anthony Gatti, Anoosha Fayyaz, Prashant Krishnamurthy, Kaushik P. Seshadreesan, Amy Babay

Year

2026

Journal

arXiv preprint

DOI

arXiv:2605.00246

arXiv

2605.00246

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their $k$-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.

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