论文标题
使用Google Scholar数据开发量子退火计算和信息的研究网络
Development of research network on Quantum Annealing Computation and Information using Google Scholar data
论文作者
论文摘要
我们从1980年初到去年，我们建立和分析了数百个顶级引用节点的网络（来自Google Scholar的研究论文和书籍；从大约44000到100）的网络。这些搜索的出版物（论文，书籍）基于四个不同集的量子退火计算和信息：a）量子/横向场旋转玻璃模型，b）量子退火，c）量子绝热计算和d）搜索出版物标题或摘要中的量子计算信息。我们将这四个类别A的年度出版物数量（$ n_p $）纳入了d $ n_p \ sim \ exp {（t/τ）} $，其中$ t $表示一年中的时间。我们发现，A类和C的缩放时间$τ$是大约10年的订单，而$τ$是B类和D的订单约5年。
We build and analyze the network of hundred top cited nodes (research papers and books from Google Scholar; strength or citation of the nodes range from about 44000 up to 100) starting early 1980 to till last year. These searched publications (papers, books) are based on Quantum Annealing Computation and Information categorized in four different sets: A) Quantum/Transverse Field Spin Glass Model, B) Quantum Annealing, C) Quantum Adiabatic Computation and D) Quantum Computation Information in the title or abstract of the searched publications. We fitted the growth in the annual number of publication ($n_p$) in each of these four categories A to D to the form $n_p \sim\exp{(t/τ)}$ where $t$ denotes the time in year. We found the scaling time $τ$ to be of order about 10 years for category A and C whereas $τ$ is order of about 5 years for category B and D.
