Compare Papers

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.

Open paper

Paper 2

Provable and scalable quantum Gaussian processes for quantum learning

Jonas Jäger, Paolo Braccia, Pablo Bermejo, Manuel G. Algaba, Diego García-Martín, M. Cerezo

Year

2026

Journal

arXiv preprint

DOI

arXiv:2605.00099

arXiv

2605.00099

Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning frameworks that are simple, interpretable, scalable, and naturally suited to quantum data. To address this, here we introduce quantum Gaussian processes, a Bayesian framework for learning from quantum systems through priors over unknown quantum transformations. We show that, under suitable conditions, unitary quantum stochastic processes define Gaussian processes, thereby enabling regression, classification, and Bayesian optimization directly on quantum data. The key ingredient in this framework is sufficient knowledge of a quantum process's structure and symmetries to define an informative prior through its corresponding quantum kernel, effectively injecting a strong, physics-informed inductive bias into the learning model. We then prove that matchgate, or free-fermionic, evolutions give rise to provable and scalable quantum Gaussian processes, providing the first family in our framework where the unknown unitary acts non-trivially on all qubits. Finally, we demonstrate accurate long-range extrapolation, phase-diagram learning in many-body systems, and sample-efficient Bayesian optimization in a quantum sensing task. Our results identify quantum Gaussian processes as a promising route toward simpler and more structured forms of quantum learning.

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