Quantum analog computing

Quantum analog computing is based upon similarity between mathematical form alism of quantum mechanics and phenomena to be computed. It exploits a dyna mical convergence of several competing phenomena to an attractor which can represent an extremum of a function, an image, a solution to a system of OD E, or a stochastic process. In this paper, a quantum version of recurrent n eural nets (QRN) as an analog computing device is discussed. This concept i s introduced by incorporating classical feedback loops into conventional qu antum networks. It is shown that the dynamical evolution of such networks, which interleave quantum evolution with measurement and reset operations, e xhibit novel dynamical properties. Moreover, decoherence in quantum recurre nt networks is less problematic than in conventional quantum network archit ectures due to the modest phase coherence times needed for network operatio n. Application of QRN to simulation of chaos, turbulence, NP-problems, as w ell as data compression demonstrate computational speedup and exponential i ncrease of information capacity. (C) 1999 Elsevier Science Ltd. All rights reserved.