  The early definition of a quantum neural network as a new field that combines
the classical neurocomputing with quantum computing was rather vague and
satisfactory in the 2000s. The widespread in 2020 modern definition of a
quantum neural network as a model or machine learning algorithm that combines
the functions of quantum computing with artificial neural networks deprives
quantum neural networks of their fundamental importance. We argue that the
concept of a quantum neural network should be defined in terms of its most
general function as a tool for representing the amplitude of an arbitrary
quantum process. Our reasoning is based on the use of the Feynman path integral
formulation in quantum mechanics. This approach has been used in many works to
investigate the main problem of quantum cosmology, such as the origin of the
Universe. In fact, the question of whether our Universe is a quantum computer
was posed by Seth Lloyd, who gave the answer is yes, but we argue that the
universe can be thought of as a quantum neural network.
