Amplitude amplification is a technique in quantum computing which generalizes the idea behind
the Grover's search algorithm, and gives rise to a family of
It was discovered by Gilles Brassard and
Peter Høyer in 1997,
and independently rediscovered by Lov Grover in 1998.
In a quantum computer, amplitude amplification can be used to obtain a
quadratic speedup over several classical algorithms.
The derivation presented here roughly follows the one given in
Assume we have an N-dimensional Hilbert space
representing the state space of our quantum system, spanned by the
orthonormal computational basis states .
Furthermore assume we have a Hermitian projection operator .
Alternatively, may be given in terms of a
Boolean oracle function
and an orthonormal operational basis
in which case
can be used to partition
into a direct sum of two mutually orthogonal subspaces,
the good subspace and
the bad subspace :
In other words, we are defining a "good subspace
via the projector
. The goal of the algorithm is then to evolve some initial state
into a state belonging to
Given a normalized state vector with nonzero overlap with both subspaces, we can uniquely decompose it as
and are the
normalized projections of into the
subspaces and ,
This decomposition defines a two-dimensional subspace
, spanned by the vectors
The probability of finding the system in a good state when measured
Define a unitary operator
flips the phase of the states in the good subspace, whereas
flips the phase of the initial state .
The action of this operator on is given by
Thus in the subspace
corresponds to a rotation by the angle :
Applying times on the state
rotating the state between the good and bad subspaces.
After iterations the probability of finding the
system in a good state is .
The probability is maximized if we choose
Up until this point each iteration increases the amplitude of the good states, hence
the name of the technique.
Assume we have an unsorted database with N elements, and an oracle function
which can recognize the good entries we are searching for, and for simplicity.
If there are good entries in the database in total, then we can find them by
initializing the quantum computer into a uniform superposition
of all the database elements,
and running the above algorithm. In this case the overlap of the initial state with the good subspace is equal to the
square root of the frequency of the good entries in the database, .
If , we can approximate the number of required iterations as
Measuring the state will now give one of the good entries with a high probability. Since each application of requires a single oracle query (assuming that the oracle is implemented as a quantum gate),
we can find a good entry with just oracle queries, thus obtaining a quadratic speedup over the best possible classical algorithm.
If we set to one, the above scenario essentially reduces to the original Grover search.