论文标题
量子退火采样
Quantum annealing sampling with a bias field
论文作者
论文摘要
偏置场的存在，编码有关目标状态的一些信息，可以增强量子优化方法的性能。在这里，我们研究了这样的偏置场对量子退火采样结果的影响，以确切的覆盖问题为例。采样是在D-Wave机器上进行的，并且针对无偏的采样程序进行了不同的偏置配置。发现偏置的退火算法对于较大的问题大小特别有效，在较大的问题大小上，偏置和目标构型之间的锤击距离变得不那么重要。这项工作激发了未来的研究工作，以在量子机本身或通过古典算法的混合方式上找到良好的偏见配置。
The presence of a bias field, encoding some information about the target state, can enhance the performance of quantum optimization methods. Here we investigate the effect of such a bias field on the outcome of quantum annealing sampling, at the example of the exact cover problem. The sampling is carried out on a D-Wave machine, and different bias configurations are benchmarked against the unbiased sampling procedure. It is found that the biased annealing algorithm works particularly well for larger problem sizes, where the Hamming distance between bias and target configuration becomes less important. This work motivates future research efforts for finding good bias configurations, either on the quantum machine itself, or in a hybrid fashion via classical algorithms.
