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- W3128543359 abstract "Quantum adversarial machine learning Is regarded as a promising approach for studying vulnerabilities of machine learning approaches in adversarial settings and developing defense solutions for adversarial inputs and manipulations in quantum systems. In this paper, we present a current status, proposed approaches and challenges in quantum adversarial machine learning by concentrating on the problems and proposed solutions. We also outline the anticipated problems and perspectives for quantum-assisted machine learning in Near-term quantum computers and limitation in datasets, applications and adversarial examples. With this article, we hope that the readers can have a more thorough understanding of quantum adversarial machine learning and the research trends in this area." @default.
- W3128543359 created "2021-02-15" @default.
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- W3128543359 date "2020-10-01" @default.
- W3128543359 modified "2023-09-27" @default.
- W3128543359 title "Quantum Adversarial Machine Learning: Status, Challenges and Perspectives" @default.
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- W3128543359 doi "https://doi.org/10.1109/tps-isa50397.2020.00026" @default.
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