论文标题
使用LHC模拟数据的量子喷气聚类
Quantum jet clustering with LHC simulated data
论文作者
论文摘要
我们研究了量子计算可以通过考虑两种可能加快经典喷气聚类算法加快的新量子算法来改善喷气聚类的情况。第一个是一个量子子例程，用于计算两个数据点之间的基于Minkowski的距离，而第二个数据点由量子电路组成，将粗糙的最大值跟踪到未分类数据列表中。当一种或两种算法以众所周知的聚类算法（k-means，亲和力传播和$ k_t $ -JET）的经典版本实现时，我们会获得与其经典对应物的效率相当的效率。此外，在前两种算法中，应用距离或最大搜索算法时，可以在维度和数据长度上提高指数速度。在$ k_t $算法中，实现了与FastJet相同订单的量子版本。
We study the case where quantum computing could improve jet clustering by considering two new quantum algorithms that might speed up classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, while the second one consists of a quantum circuit to track the rough maximum into a list of unsorted data. When one or both algorithms are implemented in classical versions of well-known clustering algorithms (K-means, Affinity Propagation and $k_T$-jet) we obtain efficiencies comparable to those of their classical counterparts. Furthermore, in the first two algorithms, an exponential speed up in dimensionality and data length can be achieved when applying the distance or the maximum search algorithm. In the $k_T$ algorithm, a quantum version of the same order as FastJet is achieved.
