Solving Power Grid Optimization Problems with Rydberg Atoms

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.