Meta-Learning Digitized-Counterdiabatic Quantum Optimization

Solving optimization tasks using variational quantum algorithms has emerged as a crucial application of the current noisy intermediate-scale quantum devices. However, these algorithms face several difficulties like finding suitable ansatz and appropriate initial parameters, among others. In this work, we tackle the problem of finding suitable initial parameters for variational optimization by employing a meta-learning technique using recurrent neural networks. We investigate this technique with the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA) that utilizes counterdiabatic protocols to improve the state-of-the-art QAOA. The combination of meta learning and DC-QAOA enables us to find optimal initial parameters for different models, such as MaxCut problem and the Sherrington-Kirkpatrick model. Decreasing the number of iterations of optimization as well as enhancing the performance, our protocol designs short depth circuit ansatz with optimal initial parameters by incorporating shortcuts-to-adiabaticity principles into machine learning methods for the near-term devices.