Concise Quantum Associative Memories with Nonlinear Search Algorithm

The model of quantum associative memories (QAM) we propose here consists in simplifying and generalizing that of Rigui Zhou \etal \cite{zhou2012} who uses the quantum matrix with binary decision diagram and nonlinear search algorithm in his model. It is worth noting that David Rosenbaum put forth the quantum matrix with binary decision diagram \cite{Rosenbaum2010} and Abrams and Llyod did the nonlinear algorithm. \cite{Abrams1998} Our model gives the possibility to retrieve one of the sought states in multi-values retrieving scheme when a measure on the first register is done. It is better than Grover's algorithm and its modified form which need $\mathcal{O}(\sqrt{\frac{2^n} {m}})$ steps when they are used as the retrieval algorithm. $n$ is the number of qubit of the first register and $m$ the number of values $x$ for which $f(x)=1$. As the nonlinearity makes the system highly susceptible to noise, an analysis of the influence of the single qubit noise channels on the Nonlinear Search Algorithm of our model of QAM, shows a fidelity of about $0.7$ whatever the number of qubits present in the first register.Comment: 23 pages, 14 figure

A note on minimax inverse generalized minimum cost flow problems

Given a generalized network and a generalized feasible flow fo, se condider a problem finding a modified edge cost d such that fo is minimum cost with repect to d and the maximum deviation between the original edge cost and e is minimum. This paper shows the relationship between this problem and minimum mean circuit problems and analyzed a binary search algorithm for this problem.Includes bibliographical reference