A continuous rosenblatt quantum perceptron

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.

Automated summary

Quantum neural networks provide a theoretical framework for implementing machine learning on quantum computers. A continuous Rosenblatt quantum perceptron, an advancement of the McCulloch-Pitts quantum perceptron, was developed and implemented on an IBM quantum computer.