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Meta-learning digitized-counterdiabatic quantum optimization
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DOI：10.1088/2058-9565/ace54a.png)
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Abstract
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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.
Keywords:
meta-learning
parameter concentration
shortcuts to adiabaticity (STA)
counterdiabatic driving
QAOA
digitized-counterdiabatic quantum computing
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