Biologically plausible learning rules for neural networks and quantum computing

Abstract Hebb's rule is assumed to be closely associated with biological learning. It has not been so far a source of powerful learning algorithms for artificial neural networks. We point to the fact that Hebb’s rule implemented in a quantum algorithm leads to learning algorithm converging much faster. The origin of the difference is “quantum entanglement”. Quantum entanglement may have a neuronal equivalent, which may be the reason why biological learning uses rules which are difficult to implement on a computer.