Preferencias Benchmarking de QAOA en el problema de reasignación laboral: un análisis empírico utilizando aprendizaje por transferencia e inicialización de TQA
Adriano Lusso, Christian Nelson Gimenez, Alejandro Mata Ali · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · 2026
In the past decade, there has been significant progress in the development of Noisy Intermediate-Scale Quantum (NISQ) computers, though further hardware improvements are necessary for large-scale quantum algorithms to execute without errors. In the meantime, researchers continue to focus on developing effective algorithms for current hardware, with an emphasis on near-term applications like combinatorial optimisation. This study presents a benchmarking analysis of the Quantum Approximate Optimisation Algorithm (QAOA) applied to the Job Reassignment Problem (JRP), which involves assigning n workers to m vacant jobs to maximise high-priority task completion and worker satisfaction. The algorithm is combined with Trotterised Quantum Annealing (TQA) initialisation and Transfer Learning, which may improve solution quality across instances. The benchmarking, performed with noiseless classical simulation on 105 JRP instances, shows promising results with approximation ratios ranging from 0.86 to 0.97. This leads to an average improvement of 12% in organisational productivity thanks to a better assignment of high-priority tasks and worker satisfaction.