论文标题
用推力进行喷气聚类的量子退火
Quantum Annealing for Jet Clustering with Thrust
论文作者
论文摘要
量子计算具有实质上加速计算昂贵的任务的希望，例如在许多元素上解决优化问题。在高能量对撞机物理学中，量子辅助算法可能会加速颗粒成喷射的聚类。在这项研究中，我们基于优化电子峰值碰撞事件中称为“推力”的数量基于喷气聚类的量子退火策略。我们发现，量子退火产生的性能与精确的经典方法和经典启发式方法相似，但仅在调整退火参数之后。没有调整，可以通过混合量子/经典方法获得可比的性能。
Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called "thrust" in electron-positron collision events. We find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, but only after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.
