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We consider a database separated into blocks. Blocks containing target items are called target blocks. Blocks without target items are called non-target blocks. We consider a case, when each target block has the same number of target items. We present a fast quantum algorithm, which finds one of the target blocks. Our algorithm is based on Grover-Radhakrishnan algorithm of partial search. We minimize the number of queries to the oracle.
Grovers algorithm achieves a quadratic speedup over classical algorithms, but it is considered necessary to know the value of $lambda$ exactly [Phys. Rev. Lett. 95, 150501 (2005); Phys. Rev. Lett. 113, 210501 (2014)], where $lambda$ is the fraction o
For the unsorted database quantum search with the unknown fraction $lambda$ of target items, there are mainly two kinds of methods, i.e., fixed-point or trail-and-error. (i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett. 113, 2105
In [Phys. Rev. Lett. 113, 210501 (2014)], to achieve the optimal fixed-point quantum search in the case of unknown fraction (denoted by $lambda$) of target items, the analytical multiphase matching (AMPM) condition has been proposed. In this paper, w
Partial search has been proposed recently for finding the target block containing a target element with fewer queries than the full Grover search algorithm which can locate the target precisely. Since such partial searches will likely be used as subr
Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users preference on target items by the items they have interacted with. Recent models use methods s