Benchmarking QAOA on the Job Reassignment Problem: An Empirical Analysis using Transfer Learning and TQA Initialisation
Year: 2026
Type: article
Abstract: 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.
Institutions: Universidad Nacional del Comahue, ITCL Technology Centre
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7134229517