ABSTRACT
Quantum mechanical counterparts of traditional neuronet models together with new retrieval paradigms are defined and studied analytically and numerically for their ability to recognize patterns. Depending on the values of parameters, storage capacity of QNNs can vary from zero up to the classical limit. Parallel processing of a quantum superposition of patterns is possible.
