A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
Financial fraud detection using quantum graph neural networks
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Q-SYNTH is a hybrid framework using a parameterized quantum circuit as the generator in a GAN to create synthetic minority-class fraud samples for tabular data, which shows reduced distribution mismatch compared to classical GANs and competitive performance in downstream detection tasks.
HQTN-SER combines a low-parameter quantum tensor network module with classical latent embeddings to reach 73-80% accuracy on three speech emotion datasets while using few qubits and showing stable training.
MADQRL distributes quantum RL across independent agents to achieve roughly 10% better performance than other distribution strategies and 5% over classical policy models in cooperative multi-agent games.
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.
citing papers explorer
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A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
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Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
Q-SYNTH is a hybrid framework using a parameterized quantum circuit as the generator in a GAN to create synthetic minority-class fraud samples for tabular data, which shows reduced distribution mismatch compared to classical GANs and competitive performance in downstream detection tasks.
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HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks
HQTN-SER combines a low-parameter quantum tensor network module with classical latent embeddings to reach 73-80% accuracy on three speech emotion datasets while using few qubits and showing stable training.
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MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
MADQRL distributes quantum RL across independent agents to achieve roughly 10% better performance than other distribution strategies and 5% over classical policy models in cooperative multi-agent games.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.