  The use of Evolutionary Algorithms (EA) for solving
Mathematical/Computational Optimization Problems is inspired by the biological
processes of Evolution. Few of the primitives involved in the Evolutionary
process/paradigm are selection of 'Fit' individuals (from a population sample)
for retention, cloning, mutation, discarding, breeding, crossover etc. In the
Evolutionary Algorithm abstraction, the individuals are deemed to be solution
candidates to an Optimization problem and additional solution(/sets) are built
by applying analogies to the above primitives (cloning, mutation etc.) by means
of evaluating a 'Fitness' function/criterion. One such algorithm is
Differential Evolution (DE) which can be used to compute the minima of
functions such as the rastrigin function and rosenbrock function. This work is
an attempt to study the result of applying the DE method on these functions
with candidate individuals generated on classical Turing modeled computation
and comparing the same with those on state of the art Quantum computation.The
study benchmarks the convergence of these functions by varying the parameters
initialized and reports timing, convergence, and resource utilization results.
