Performance Comparison of Gate-Based and Adiabatic Quantum Computing for Power Flow Analysis

In this paper, we present the first direct comparison between gate-based quantum computing (GQC) and adiabatic quantum computing (AQC) for solving the AC power flow (PF) equations. Building on the Adiabatic Quantum Power Flow (AQPF) algorithm originally designed for annealing platforms, we adapt it to the Quantum Approximate Optimization Algorithm (QAOA). The PF equations are reformulated as a combinatorial optimization problem. Numerical experiments on a 4-bus test system assess solution accuracy and computational time. Results from QAOA are benchmarked against those obtained using D-Wave's Advantage system and Fujitsu's latest generation Digital Annealer, i.e., Quantum-Inspired Integrated Optimization software (QIIO). The findings provide quantitative insights into the performance trade-offs, scalability, and practical viability of GQC versus AQC paradigms for PF analysis, highlighting the potential of quantum algorithms to address the computational challenges associated with modern electricity networks in the Noisy Intermediate-Scale Quantum (NISQ).