Abstract
We report the effects of a simple decoherence model on the quantum search algorithm. Despite its simplicity, the decoherence model is an instructive model that can genuinely imitate realistic noisy environment in many situations. As one would expect, as the size of the database gets larger, the effects of decoherence on the efficiency of the quantum search algorithm cannot be ignored. Moreover, with decoherence, it may not be useful to iterate beyond the first maxima in the probability distribution of the search entry. Surprisingly, we also find that the number of iterations for maximum probability of the search entry reduces with decoherence.
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