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
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.
Sau Lan Wu Jay Chan Wen Guan Shaojun Sun Alex Wang Chen Zhou Miron Livny Federico Carminati Alberto Di Meglio Andy C. Y. Li Joseph Lykken Panagiotis Spentzouris Samuel Yen-Chi Chen Shinjae Yoo Tzu-Chieh Wei