Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits
Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits
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基于量子变分分类器方法的量子机器学习在IBM量子计算机模拟器和10量子位硬件上的高能物理分析中的应用
DOI：
10.1088/1361-6471/ac1391
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发表时间：
2020-12
期刊：
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通讯作者：
S. Wu;J. Chan;W. Guan;Shaojun Sun;A. Wang;Chengda Zhou;M. Livny;F. Carminati;A. D. Meglio;A. Li;J. Lykken;P. Spentzouris;Samuel Yen-Chi Chen;Shinjae Yoo;T. Wei
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文献类型：
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作者：
S. Wu;J. Chan;W. Guan;Shaojun Sun;A. Wang;Chengda Zhou;M. Livny;F. Carminati;A. D. Meglio;A. Li;J. Lykken;P. Spentzouris;Samuel Yen-Chi Chen;Shinjae Yoo;T. Wei
大型强子对撞机实验项目的主要目标之一是发现新物理学。这需要识别巨大背景中的稀有信号。使用机器学习算法大大增强了我们实现这一目标的能力。随着量子技术的进步，量子机器学习可能成为高能物理数据分析的强大工具。在本研究中，使用 IBM 门模型量子计算系统，我们在最近的两项大型强子对撞机旗舰物理分析中采用了量子变分分类器方法：$t\bar{t}H$（与顶夸克对相关的希格斯玻色子产生）和 $H\rightarrow\mu^{+}\mu^{-}$（希格斯玻色子衰变为两个 μ 子，探测希格斯玻色子与第二代费米子的耦合）。我们已经在 IBM 量子模拟器和 IBM 量子硬件上获得了 10 个量子位的早期结果。在量子模拟器上使用 100 个事件的小训练样本，量子变分分类器方法的性能与 LHC 物理分析中经常采用的 SVM（支持向量机）和 BDT（增强决策树）等经典算法类似。在量子硬件上，量子变分分类器方法显示出了可与量子模拟器相媲美的良好判别能力。这项研究表明，量子机器学习能够区分现实物理数据集中的信号和背景。我们预见量子机器学习在未来高亮度大型强子对撞机物理分析中的应用，包括希格斯玻色子自耦合的测量和暗物质的搜索。
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu^{+}\mu^{-}$ (Higgs boson decays to two muons, probing the Higgs boson couplings to second-generation fermions). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. With small training samples of 100 events on the quantum simulator, the quantum variational classifier method performs similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets. We foresee the usage of quantum machine learning in future high-luminosity LHC physics analyses, including measurements of the Higgs boson self-couplings and searches for dark matter.