  Major players in the global aerospace industry are shifting their focus
toward achieving net carbon-neutral operations by 2050. A considerable portion
of the overall carbon emission reduction is expected to come from new aircraft
technologies, such as flight path optimization. In pursuing these
sustainability objectives, we delve into the capacity of quantum computing to
tackle computational challenges associated with flight path optimization, an
essential operation within the aerospace engineering domain with important
ecological and economic considerations. In recent years, the quantum computing
field has made significant strides, paving the way for improved performance
over classical algorithms. In order to effectively apply quantum algorithms in
real-world scenarios, it is crucial to thoroughly examine and tackle the
intrinsic overheads and constraints that exist in the present implementations
of these algorithms. Our study delves into the application of quantum computers
in flight path optimization problems and introduces a customizable modular
framework designed to accommodate specific simulation requirements. We examine
the running time of a hybrid quantum-classical algorithm across various quantum
architectures and their simulations on CPUs and GPUs. A temporal comparison
between the conventional classical algorithm and its quantum-improved
counterpart indicates that achieving the theoretical speedup in practice may
necessitate further innovation. We present our results from running the quantum
algorithms on IBM hardware and discuss potential approaches to accelerate the
incorporation of quantum algorithms within the problem domain.
