论文标题
通过量子近似优化算法搜索Ferrum链的旋转配置
Searching for Possible Spin Configurations of Ferrum Chain via Quantum Approximate Optimization Algorithm
论文作者
论文摘要
通过交换相互作用相互相互作用，计算链的预期旋转配置从根本上讲是一个配置优化问题。量子近似优化算法是在量子设备上配置此类系统的合适候选者。在这项工作中，我们考虑了三个不同长度的渡轮链，并使用量子近似优化算法计算了其最可探测的自旋构型。我们使用量子馈电神经网络作为量子近似优化算法的优化器。我们已经成功地获得了最长的Ferrum链的预期自旋构型。
Calculating the expected spin configuration of the chain consisting of Ferrum atoms interacting with each other through exchange interaction is fundamentally a configuration optimization problem. Quantum Approximate Optimization Algorithm is a suitable candidate to configure such systems on a quantum device. In this work we have considered Ferrum chains of three different lengths and calculated their most-probable spin configurations using Quantum Approximate Optimization Algorithm. We employed a Quantum Feed Forward Neural Network as the optimizer of Quantum Approximate Optimization Algorithm. We have successfully obtained the expected spin configuration for the longest Ferrum Chain.
