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Quantum Amplitude Amplification and Estimation
Consider a Boolean function that partitions set
between its good and bad elements, where is good if and bad
otherwise. Consider also a quantum algorithm such that is a quantum superposition of the
elements of , and let denote the probability that a good element is
produced if is measured. If we repeat the process of running ,
measuring the output, and using to check the validity of the result, we
shall expect to repeat times on the average before a solution is found.
*Amplitude amplification* is a process that allows to find a good after an
expected number of applications of and its inverse which is proportional to
, assuming algorithm makes no measurements. This is a
generalization of Grover's searching algorithm in which was restricted to
producing an equal superposition of all members of and we had a promise
that a single existed such that . Our algorithm works whether or
not the value of is known ahead of time. In case the value of is known,
we can find a good after a number of applications of and its inverse
which is proportional to even in the worst case. We show that this
quadratic speedup can also be obtained for a large family of search problems
for which good classical heuristics exist. Finally, as our main result, we
combine ideas from Grover's and Shor's quantum algorithms to perform amplitude
estimation, a process that allows to estimate the value of . We apply
amplitude estimation to the problem of *approximate counting*, in which we wish
to estimate the number of such that . We obtain optimal
quantum algorithms in a variety of settings.Comment: 32 pages, no figure
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