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We present a quantum probabilistic associative memory using the inverse of quantum Fourier transform and Grover's algorithm to recover existing or similar patterns in the memory. The content of the memory is created using a generator of a superposition state representing a given set of patterns. We discuss the architecture of the proposed memory including the storing, recovering and processing of similarity tolerance of the input query. The associative memory can extrapolate and recover similar stored patterns. The system is unitary and runs in O (n) steps, where n is the number of qubits of the patterns. [ABSTRACT FROM AUTHOR]

In order to investigate the dynamics of a quantum weightless neuron node we feed its output back as input. Due to the fact that controlled operators used in the neuron circuit usually generate entanglement, we propose a mathematical method to extract the output at time t and build from that output the input at time t + 1 . As a result the time evolution is a real-valued nonlinear map with one real parameter. The dynamics orbits are plotted showing acute sensitivity to initial conditions clearly exhibiting nonlinearity by just looking at amplitude graphs. The fractal geometry and regions of convergence are discussed by their Julia Set images and a new measure for model comparison is put forward. [ABSTRACT FROM AUTHOR]