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• W3101804921 endingPage "30004" @default.
• W3101804921 startingPage "30004" @default.
• W3101804921 abstract "We demonstrate that it is possible to implement a quantum perceptron with a sigmoid activation function as an efficient, reversible many-body unitary operation. When inserted in a neural network, the perceptron's response is parameterized by the potential exerted by other neurons. We prove that such a quantum neural network is a universal approximator of continuous functions, with at least the same power as classical neural networks. While engineering general perceptrons is a challenging control problem --also defined in this work--, the ubiquitous sigmoid-response neuron can be implemented as a quasi-adiabatic passage with an Ising model. In this construct, the scaling of resources is favorable with respect to the total network size and is dominated by the number of layers. We expect that our sigmoid perceptron will have applications also in quantum sensing or variational estimation of many-body Hamiltonians." @default.
• W3101804921 created "2020-11-23" @default.
• W3101804921 creator A5033237621 @default.
• W3101804921 creator A5044905946 @default.
• W3101804921 date "2019-03-04" @default.
• W3101804921 modified "2023-10-17" @default.
• W3101804921 title "Unitary quantum perceptron as efficient universal approximator" @default.
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• W3101804921 doi "https://doi.org/10.1209/0295-5075/125/30004" @default.
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