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Machine learning algorithms are becoming increasingly prevalent in everyday life, but they are vulnerable to data manipulation attacks. In an article published in Nature Machine Intelligence, my colleagues and I propose that the integration of quantum computing in these models could yield new algorithms with strong resilience against adversarial attacks. Data manipulation attacks can be launched in several ways, and the consequences of these attacks can be severe. Our proposed solution could help protect against these attacks and ensure the security of machine learning algorithms.
