Each independent run uses one oracle call and gives a uniformly sampled equation . Collect linearly independent equations and use Gaussian elimination over . Their solution space has dimension one and consists of , so the promised nonzero solution identifies uniquely.
To quantify the expected query count for Simon sampling, write . If the existing equations have rank , their span contains of the possible samples. A fresh equation raises the rank with probability . The mean waiting time is the corresponding geometric distribution mean, givingThe last sum is less than : its first term is and its remaining terms are bounded by . Hence the expected number of quantum queries is . For a fixed high-confidence budget, use samples. Failure to span means that a nonzero linear functional on the -dimensional sampling space annihilates every sample. Each such functional has probability , so the union bound gives failure probability at most . Thus queries suffice for failure probability at most . For , the nonzero string is known already and no samples are needed.
Classically, a collision between distinct queried arguments immediately yields . Random queries produce this collision after order calls, by the birthday problem. Conversely, choose the nonzero period uniformly and label the fibres randomly. Before a collision, queries can exclude at most periods, their pairwise argument differences; every remaining period has the same no-collision transcript distribution. When , those exclusions occupy a vanishing fraction of the candidate periods, and neither collision discovery nor guessing a remaining period has a constant success probability. More quantitatively, conditioned on no previous collision, the next query tests at most of at least remaining periods; summing these conditional probabilities proves the same square-root lower bound. Therefore the optimal bounded-error classical query count is , exponentially larger in than the quantum count.
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