Abstract
Quantum neural networks are expected to provide the theoretical framework to implement machine learning on quantum computers. We develop a continuous Rosenblatt quantum perceptron which represents the generalization of the McCulloch–Pitts quantum perceptron existing in the literature. We implement its quantum circuit on a IBM quantum computer.
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