parameterized quantum circuits can be used as quantum neural networks and
have the potential to outperform their classical counterparts when trained for
addressing learning problems. To date, much of the results
本文提出一种使用经典神经网络协助量子学习的元学习方法，通过训练经典递归神经网络对 Quantum Approximate Optimization Algorithm (QAOA) for MaxCut，QAOA for Sherrington-Kirkpatrick Ising model 以及 Hubbard model 的参数进行快速优化，以减少优化迭代次数。同时，发现该方法可以推广到其他问题类型，使得量子学习更加高效。