Meta-learning digitized-counterdiabatic quantum optimization

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

Automated summary

In this paper, we use meta-learning with recurrent neural networks to address the difficulties in finding suitable variational parameters and initial parameters for the QAOA. By combining meta-learning and counterdiabaticity, we find suitable variational parameters and reduce the number of optimization iterations required. Our method improves the performance of the state-of-the-art QAOA by offering a short-depth circuit ansatz with optimal initial parameters.