Quantum probabilistic associative memory architecture

Abstract 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.