Variational Quantum Classifiers
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Quantum classifiers show inherent defense against gradient attacks due to measurement costs
Researchers have analyzed the cost of adversarial attacks against quantum classifiers, finding that finite quantum measurement statistics, or shot noise, can act as a defense mechanism. The study quantifies the measurem…
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Quantum machine learning papers tackle noise and reliability
Two new research papers explore advancements in quantum machine learning, focusing on enhancing reliability and uncertainty quantification. The first paper introduces a variational quantum classifier that uses amplitude…
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Quantum autoencoders enhance vision learning and defend against adversarial attacks
Researchers have developed quantum masked autoencoders (QMAEs) capable of learning missing features within quantum states, outperforming standard quantum autoencoders in image reconstruction tasks. Additionally, a new d…