Quantum neurocomputation and signal processing

We consider a quantum computational algorithm that can be used to determine (probabilistically) how close a given signal is to one of a set of previously observed signals stored in the state of a quantum neurocomputional machine. The realization of a new quantum algorithm for factorization of integers by Shor and its implication to cryptography has created a rapidly growing field of investigation. Although no physical realization of a quantum computer is available, a number of software systems simulating a quantum computation process exist. In light of the rapidly increasing power of desktop computers and their ability to carry out these simulations, it is worthwhile to investigate possible advantages as well as realizations of quantum algorithms in signal processing applications. The algorithm presented offers a glimpse of the potential of this approach. Neural networks (NN) provide a natural paradigm for parallel and distributed processing of a wide class of signals. Neural networks within the context of classical computation have been used for approximation and classification tasks with some success. We propose a model for quantum neurocomputation (QN) and explore some of its properties and potential applications to signal processing in an information theoretic context