TY - JOUR
A2 - Yuan, Gonglin
AU - Wang, Haibin
AU - Zhao, Jiaojiao
AU - Wang, Bosi
AU - Tong, Lian
PY - 2021
DA - 2021/03/25
TI - A Quantum Approximate Optimization Algorithm with Metalearning for MaxCut Problem and Its Simulation via TensorFlow Quantum
SP - 6655455
VL - 2021
AB - A quantum approximate optimization algorithm (QAOA) is a polynomial-time approximate optimization algorithm used to solve combinatorial optimization problems. However, the existing QAOA algorithms have poor generalization performance in finding an optimal solution from a feasible solution set of combinatorial problems. In order to solve this problem, a quantum approximate optimization algorithm with metalearning for the MaxCut problem (MetaQAOA) is proposed. Specifically, a quantum neural network (QNN) is constructed in the form of the parameterized quantum circuit to detect different topological phases of matter, and a classical long short-term memory (LSTM) neural network is used as a black-box optimizer, which can quickly assist QNN to find the approximate optimal QAOA parameters. The experiment simulation via TensorFlow Quantum (TFQ) shows that MetaQAOA requires fewer iterations to reach the threshold of the loss function, and the threshold of the loss value after training is smaller than comparison methods. In addition, our algorithm can learn parameter update heuristics which can generalize to larger system sizes and still outperform other initialization strategies of this scale.
SN - 1024-123X
UR - https://doi.org/10.1155/2021/6655455
DO - 10.1155/2021/6655455
JF - Mathematical Problems in Engineering
PB - Hindawi
KW -
ER -