论文标题
量子退火学习搜索实现
Quantum Annealing Learning Search Implementations
论文作者
论文摘要
本文介绍了混合量子量子算法量子量子退火学习搜索（QALS）的两个实现（以C ++和Python）的详细信息和测试。 QALS于2019年提出是一种新技术，以解决无法直接表示D-Wave机器硬件体系结构的一般QUBO问题。在经典迭代结构中重复调用量子机和相关的融合证明，启动了一种学习机制，以在量子体系结构中找到给定问题的编码。目前的工作考虑了QALS测试的数字分区问题（NPP）和旅行推销员问题（TSP）。结果结果是非常出乎意料的，QALS无法执行其他考虑的方法，尤其是在NPP中，经典方法通常超过量子量的质量退火。然而，查看TSP测试，QALS实现了其主要目标，即处理QUBO问题，无法直接映射QPU拓扑。
This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to solve general QUBO problems that cannot be directly represented into the hardware architecture of a D-Wave machine. Repeated calls to the quantum machine within a classical iterative structure and a related convergence proof originate a learning mechanism to find an encoding of a given problem into the quantum architecture. The present work considers the Number Partitioning Problem (NPP) and the Travelling Salesman Problem (TSP) for the testing of QALS. The results turn out to be quite unexpected, with QALS not being able to perform as well as the other considered methods, especially in NPP, where classical methods outperform quantum annealing in general. Nevertheless, looking at the TSP tests, QALS has fulfilled its primary goal, i.e., processing QUBO problems not directly mappable to the QPU topology.
