Abstract
Grover’s Algorithm finds a unique element in an unsorted stock of \(N\)-elements in \(\sqrt{N}\) queries through quantum search. A single-query solution can also be designed, but with an overhead of \(N\log _2 N\) steps to prepare and post process the query, which is worse than the classical \(N/2\) queries. We show here that by distributing the computing load on a set of quantum computers, we achieve better information theoretic bounds and relaxed space scaling. Howsoever small one quantum computing node is, by virtue of networking and sharing of data, we can virtually work with a sufficiently large qubit space.
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Goswami, D. (2014). Quantum Distributed Computing Applied to Grover’s Search Algorithm. In: Calude, C., Freivalds, R., Kazuo, I. (eds) Computing with New Resources. Lecture Notes in Computer Science(), vol 8808. Springer, Cham. https://doi.org/10.1007/978-3-319-13350-8_14
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DOI: https://doi.org/10.1007/978-3-319-13350-8_14
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