Biologically plausible learning rules for neural networks and quantum computing

Hebb's rule is assumed to be closely associated with biological learning. I t has not been so far a source of powerful learning algorithms for artifici al neural networks. We point to the fact that Hebb's rule implemented in a quantum algorithm leads to learning algorithm converging much faster. The o rigin of the difference is "quantum entanglement". Quantum entanglement may have a neuronal equivalent, which may be the reason why biological learnin g uses rules which are difficult to implement on a computer. (C) 2000 Elsev ier Science B.V. All rights reserved.