  The rapid development of neutral atom quantum hardware provides a unique
opportunity to design hardware-centered algorithms for solving real-world
problems aimed at establishing quantum utility. In this work, we study the
performance of two such algorithms on solving MaxCut problem for various
weighted graphs. The first method uses a state-of-the-art machine learning tool
to optimize the pulse shape and embedding of the graph using an adiabatic
Ansatz to find the ground state. We tested the performance of this method on
finding maximum power section task of the IEEE 9-bus power system and obtaining
MaxCut of randomly generated problems of size up to 12 on the Aquila quantum
processor. To the best of our knowledge, this work presents the first MaxCut
results on Quera's Aquila quantum hardware. Our experiments run on Aquila
demonstrate that even though the probability of obtaining the solution is
reduced, one can still solve the MaxCut problem on cloud-accessed neutral atom
quantum hardware. The second method uses local detuning, which is an emergent
update on the Aquila hardware, to obtain a near exact realization of the
standard QAOA Ansatz with similar performance. Finally, we study the fidelity
throughout the time evolution realized in the adiabatic method as a benchmark
for the IEEE 9-bus power grid graph state.
