Quantum Search with Advice
We consider the problem of search of an unstructured list for a marked element x, when one is given advice as to where x might be located, in the form of a probability distribution. The goal is to minimise the expected number of queries to the list made to find x, with respect to this distribution. We present a quantum algorithm which solves this problem using an optimal number of queries, up to a constant factor. For some distributions on the input, such as certain power law distributions, the algorithm can achieve exponential speed-ups over the best possible classical algorithm. We also give an efficient quantum algorithm for a variant of this task where the distribution is not known in advance, but must be queried at an additional cost. The algorithms are based on the use of Grover’s quantum search algorithm and amplitude amplification as subroutines.
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