论文标题
用于全局优化的变分量子迭代功率算法
Variational quantum iterative power algorithms for global optimization
论文作者
论文摘要
我们介绍了一种称为量子迭代功率算法（QIPA）的变异量子算法系列，该算法的表现优于现有的近期近期量子算法。我们演示了QIPA的功能，适用于三个不同的全局优化数值实验：$ H_2 $分子解离的基础状态优化，对Transmon Qubit地下态的搜索和二杆菌分解。由于我们的算法是混合的，因此可以轻松地将量子/经典技术（例如缓解误差和自适应变分ansatzes）纳入算法中。由于浅量子电路的要求，我们预计在当前主要量子硬件中将大规模实施和采用拟议算法。
We introduce a family of variational quantum algorithms called quantum iterative power algorithms (QIPA) that outperform existing hybrid near-term quantum algorithms of the same kind. We demonstrate the capabilities of QIPA as applied to three different global-optimization numerical experiments: the ground-state optimization of the $H_2$ molecular dissociation, search of the transmon qubit ground-state, and biprime factorization. Since our algorithm is hybrid, quantum/classical technologies such as error mitigation and adaptive variational ansatzes can easily be incorporated into the algorithm. Due to the shallow quantum circuit requirements, we anticipate large-scale implementation and adoption of the proposed algorithm across current major quantum hardware.
