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論文情報
・url:
・title: Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
・abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cause the model to make mistakes. Quantum machine learning models are also
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