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.
Quantum generative adversarial learning,
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Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.
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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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Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.