论文标题
数据驱动的量子近似优化算法用于网络物理功率系统
Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems
论文作者
论文摘要
量子技术提供了一种开创性的方法，可以解决电力系统中具有挑战性的计算问题，尤其是针对分布式能源资源（DERS）主导的网络物理系统，这些系统已广泛开发以促进能源可持续性。系统的最大功率或数据部分对于监视，操作和控制至关重要，而需要高度的计算工作。量子近似优化算法（QAOA）提供了一种有希望的方法，可以通过利用量子资源来搜索这些部分。但是，其性能高度依赖于关键参数，尤其是加权图。我们提出了一个数据驱动的QAOA，该QAOA基于归一化图密度在加权图之间传输准最佳参数，并使用39,774个实例验证策略。没有参数优化，我们的数据驱动的QAOA与Goemans-Williamson算法相当。这项工作推动了QAOA和飞行员在嘈杂的中间量子设备中量子技术的实际应用，并预示了其在量子时代的下一代计算。
Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems, especially for Distributed Energy Resources (DERs) dominant cyber-physical systems that have been widely developed to promote energy sustainability. The systems' maximum power or data sections are essential for monitoring, operation, and control, while high computational effort is required. Quantum Approximate Optimization Algorithm (QAOA) provides a promising means to search for these sections by leveraging quantum resources. However, its performance highly relies on the critical parameters, especially for weighted graphs. We present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density, and verify the strategy with 39,774 instances. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots the practical application of quantum technique to power systems in noisy intermediate-scale quantum devices, heralding its next-generation computation in the quantum era.
