ParaQuanNet distinguishes eight quantum generative circuits via 99.5% accurate classification of their output data using parallel quantum embeddings and mutually unbiased measurements.
arXiv preprint arXiv:2511.00406 (2025)
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Machine unlearning methods adapted to hybrid quantum models achieve effective forgetting that varies with circuit depth and entanglement, establishing initial empirical baselines for quantum-aware unlearning.
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Identification of quantum generative circuits with parallel quantum neural network
ParaQuanNet distinguishes eight quantum generative circuits via 99.5% accurate classification of their output data using parallel quantum embeddings and mutually unbiased measurements.
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Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study
Machine unlearning methods adapted to hybrid quantum models achieve effective forgetting that varies with circuit depth and entanglement, establishing initial empirical baselines for quantum-aware unlearning.