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Application of Quantum Machine Learning to High Energy Physics Analysis at the LHC using Quantum Computer Simulators and Hardware （量子机器学习在LHC高能物理数据分析中的应用 (使用量子计算机模拟器和硬件)
by
DrChen Zhou
(University of Wisconsin)
→
Asia/Shanghai
B326 (IHEP)
B326
IHEP
Description
加入 Zoom 会议
https://weidijia.zoom.com.cn/j/61784019425?pwd=WTl6VThicEJ6ZlZHdStXR1hjcmJNUT09
会议号：617 8401 9425
密码：2021
abstract:
Quantum computing may offer speed-up for HEP data analysis at HL-LHC, and can be a new computational paradigm for big data analysis in High Energy Physics. We have employed Variational Quantum Classifier (VQC) method, Quantum Support Vector Machine Kernel (QSVM-kernel) method and Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->di-muon (Higgs decay to two muons). I will present our experiences and results of a study on LHC High Energy Physics data analysis with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service).
量子计算可能加快HL-LHC的高能物理数据分析的速度，并且可能成为高能物理大数据分析的计算方面的新典范。 我们已应用Variational Quantum Classifier (VQC)方法，Quantum Support Vector Machine Kernel (QSVM-kernel) 方法和Quantum Neural Network (QNN)方法到LHC的两个重要物理分析：ttH（希格斯与两个顶夸克的联合产生）和H->di-muon（希格斯衰变到两个muon）。 我将介绍我们使用IBM量子计算机模拟器和量子计算机硬件（使用IBM Qiskit框架），Google量子计算机模拟器（使用Google Cirq框架）和Amazon量子计算机模拟器（使用Amazon Braket框架）进行LHC高能物理数据分析研究的经验和结果。
简介：
After four years at Peking University as an undergrad student majoring in physics and six years at Duke University as a PhD student working on the ATLAS and CDF experiments, I became a postdoc research associate in the ATLAS group of University of Wisconsin in summer 2016. During my postdoc time, I have made contributions to ATLAS physics analyses (including ttH production, Higgs->di-muon decay, Higgs coupling properties, di-Higgs production, Dark Matter search with mono-Higgs, and high-mass resonance), ATLAS phase II pixel detector upgrade, and application of quantum machine learning to high energy physics analysis.
报告人于2006-2010年在北京大学物理学院攻读本科，并于2010-2016年在杜克大学物理系攻读博士（参加ATLAS和CDF实验）。我于2016年夏天成为威斯康星大学ATLAS组的博士后。在做博士后的几年里，我致力于ATLAS物理分析（包括ttH产生, Higgs->di-muon衰变, Higgs耦合性质, di-Higgs产生, mono-Higgs暗物质寻找, 高质量共振态），ATLAS 硅像素探测器的升级，以及将量子机器学习应用于高能物理分析。
