  Given a parameterized quantum circuit such that a certain setting of these
real-valued parameters corresponds to Grover's celebrated search algorithm, can
a variational algorithm recover these settings and hence learn Grover's
algorithm? We studied several constrained variations of this problem and
answered this question in the affirmative, with some caveats. Grover's quantum
search algorithm is optimal up to a constant. The success probability of
Grover's algorithm goes from unity for two-qubits, decreases for three- and
four-qubits and returns near unity for five-qubits then oscillates
ever-so-close to unity, reaching unity in the infinite qubit limit. The
variationally approach employed here found an experimentally discernible
improvement of $5.77\%$ and $3.95\%$ for three- and four-qubits respectively.
Our findings are interesting as an extreme example of variational search, and
illustrate the promise of using hybrid quantum classical approaches to improve
quantum algorithms. This paper further demonstrates that to find optimal
parameters one doesn't need to vary over a family of quantum circuits to find
an optimal solution. This result looks promising and points out that there is a
set of variational quantum problems with parameters that can be efficiently
found on a classical computer for arbitrary number of qubits.
