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.
LNCC-National Laboratory for Scientific Computing -
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