Meta-learning digitized-counterdiabatic quantum optimization

Abstract The use of variational quantum algorithms for optimization tasks has emerged as a crucial application the current noisy intermediate-scale computers. However, these face significant difficulties in finding suitable ansatz and appropriate initial parameters. In this paper, we employ meta-learning using recurrent neural networks to address issues recently proposed digitized-counterdiabatic approximate algorithm (QAOA). By combining counterdiabaticity, find parameters reduce number iterations required. We demonstrate effectiveness our approach by applying it MaxCut problem Sherrington–Kirkpatrick model. Our method offers short-depth circuit with optimal parameters, thus improving performance state-of-the-art QAOA.