There has been a growing interest in artificial neural networks (ANNs)
based on quantum theoretical concepts and techniques due to cognitive and
computer science aspects.
The so called Quantum Neural Networks (QNNs) is a promising area in
the field of quantum computation and quantum information. However, a key
questions about QNNs is what such an architecture will look like as an
implementation on quantum hardware. To answer this question we firstly
observe that QNNs needs nonlinear effects to be implemented. Based on this
consideration, we discuss a system composed by a quantum dot molecule coupled
to its environment and subject to a time-varying external field. A discretized
version of the Feynman path integral formulation for this system can be
put into a form that resembles a classical neural network. Starting from
this interpretation, we discuss the learning rules and nonlinearity in
the context of QNNs.
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