  We describe a model element able to perform universal stochastic
approximations of continuous multivariable functions in both neuron-like and
quantum form. The implementation of this model in the form of a multi-barrier,
multiple-slit system is proposed and it is demonstrated that this single
neuron-like model is able to perform the XOR function unrealizable with single
classical neuron. For the simplified waveguide variant of this model it is
proved for different interfering quantum alternatives with no correlated
adjustable parameters, that the system can approximate any continuous function
of many variables. This theorem is applied to the 2-input quantum neural model
based on the use of the schemes developed for controlled nonlinear multiphoton
absorption of light by quantum systems. The relation between the field of
quantum neural computing and quantum control is discussed.
