842 research outputs found
A Simple Quantum Neural Net with a Periodic Activation Function
In this paper, we propose a simple neural net that requires only
number of qubits and quantum gates: Here, is the number of input
parameters, and is the number of weights applied to these parameters in the
proposed neural net. We describe the network in terms of a quantum circuit, and
then draw its equivalent classical neural net which involves nodes in
the hidden layer. Then, we show that the network uses a periodic activation
function of cosine values of the linear combinations of the inputs and weights.
The backpropagation is described through the gradient descent, and then iris
and breast cancer datasets are used for the simulations. The numerical results
indicate the network can be used in machine learning problems and it may
provide exponential speedup over the same structured classical neural net.Comment: a discussion session is added. 5 pages, conference paper. To appear
in The 2018 IEEE International Conference on Systems, Man, and Cybernetics
(SMC2018
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