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Artificial Neural Nets and Genetic Algorithms

A Quantum Associative Memory Based on Grover’s Algorithm

Dan A. Ventura, Tony R. Martinez

Published Jan 1, 1999• 22 citations
Publisher
DOI
10.1007/978-3-7091-6384-9_5

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We describe a quantum algorithm for computing the intersection of two sets and its application to associative memory. The algorithm is based on a modification of Grover's quantum search algorithm (Grover, 1996). We present algorithms for pattern retrieval, pattern completion, and pattern correction. We show that the quantum associative memory can store an exponential number of memories and retrieve them in sub-exponential time. We prove that this model has advantages over known classical associative memories as well as previously proposed quantum models.
An approach to constructing an artificial quantum associative memory based on entanglement (QuEAM) is discussed. The QuEAM is an exponential increase in the capacity of the memory when compared to classical associative memories such as the Hopfield network. According to the characteristics of amount of entanglement, the study algorithm based on Grover's well-known algorithm is locally magnified the probability amplitude for the qubit. The basic principle of entangled states is discussed. Concrete examples illustrating the properties of the proposed model are also presented.