  Amplitude Amplification -- a key component of Grover's Search algorithm --
uses an iterative approach to systematically increase the probability of one or
multiple target states. We present novel strategies to enhance the
amplification procedure by partitioning the states into classes, whose
probabilities are increased at different levels before or during amplification.
The partitioning process is based on the binomial distribution. If the classes
to which the search target states belong are known in advance, the number of
iterations in the Amplitude Amplification algorithm can be drastically reduced
compared to the standard version. In the more likely case in which the relevant
classes are not known in advance, their selection can be configured at run
time, or a random approach can be employed, similar to classical algorithms
such as binary search. In particular, we apply this method in the context of
our previously introduced Quantum Dictionary pattern, where keys and values are
encoded in two separate registers, and the value-encoding method is independent
of the type of superposition used in the key register. We consider this type of
structure to be the natural setup for search. We confirm the validity of our
new approach through experimental results obtained on real quantum hardware,
the Honeywell System Model H0 trapped-ion quantum computer.
