Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem

ArXi:2412.21071v2 Announce Type: replace-cross The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful in solving combinatorial optimization problems (COPs). It has been observed that the optimal parameters obtained from one instance of a COP can be transferred to another instance, resulting in generally good solutions for the latter.