Leveraging Full Data Utilization for Enhanced Performance in Quantum Approximate Optimization Algorithms

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid classical-quantum approach for addressing complex problems by integrating classical and quantum computing resources. A quantum circuit with classical parameters is executed. Using the most likely measurement output, the parametners are optimized with classical methods. Finally, one iterates until convergence. We address the challenge of flat probability distributions for large system sizes, where choosing the most likely bitstring becomes impractical. Standard QAOA typically discards intermediate values for the cost of each basis state. In contrast, we retain the smallest cost basis state throughout the algorithm's iterations. By focusing on this tailored approach, this adaptation of QAOA consistently delivers improved solutions without incurring additional computational or financial cost. This research demonstrates the algorithm's reliability and reproducibility through the lens of the aircraft loading optimization problem of Airbus's 2020 Quantum Comptuing Challenge executed on Rigetti's Aspen M-3 quantum processor.