论文标题
对撞机物理的量子异常检测
Quantum Anomaly Detection for Collider Physics
论文作者
论文摘要
Quantum机器学习（QML）是一种令人兴奋的工具，在量子计算硬件方面的进步部分受到了最新的关注。尽管目前尚无正式保证QML在相关问题上优于经典ML，但高能量物理数据集有许多关于经验优势的主张。这些研究通常不会在训练中声称指数加速，而是通过有限的培训数据专注于改进的性能。我们探索以低统计数据集为特征的分析。特别是，我们研究了一个受小数据集限制的大型强子对撞机的四局最终状态中的异常检测任务。我们在半监督模式下探索QML的应用，以寻找新物理，而无需指定特定的信号模型假设。我们没有发现QML比经典ML提供任何优势。可能是，将来将建立QML优于古典ML对撞机物理学的ML，但是目前，古典ML是一种强大的工具，它将继续扩展LHC及其他地区的科学。
Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an exponential speedup in training, but instead usually focus on an improved performance with limited training data. We explore an analysis that is characterized by a low statistics dataset. In particular, we study an anomaly detection task in the four-lepton final state at the Large Hadron Collider that is limited by a small dataset. We explore the application of QML in a semi-supervised mode to look for new physics without specifying a particular signal model hypothesis. We find no evidence that QML provides any advantage over classical ML. It could be that a case where QML is superior to classical ML for collider physics will be established in the future, but for now, classical ML is a powerful tool that will continue to expand the science of the LHC and beyond.
