Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC

• URL: http://arxiv.org/abs/2104.05059v2
• Date: Thu, 9 Sep 2021 15:47:58 GMT
• Title: Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• Authors: Sau Lan Wu, Shaojun Sun, Wen Guan, Chen Zhou, Jay Chan, Chi Lung Cheng, Tuan Pham, Yan Qian, Alex Zeng Wang, Rui Zhang, Miron Livny, Jennifer Glick, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli, Federico Carminati, Alberto Di Meglio, Andy C. Y. Li, Joseph Lykken, Panagiotis Spentzouris, Samuel Yen-Chi Chen, Shinjae Yoo, Tzu-Chieh Wei
• Abstract summary: We employ a support vector machine with a quantum kernel estimator to a recent LHC flagship physics analysis: $tbartH$. In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM- Kernel method performs as well as its classical counterparts. The application of the QSVM- Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator.
• Score: 8.428528868905643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum and Amazon Braket. Additionally, using 15 qubits and 100 events, the application of the QSVM-Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator. Our study confirms that the QSVM-Kernel method can use the large dimensionality of the quantum Hilbert space to replace the classical feature space in realistic physics datasets.

Related papers

• Technology and Performance Benchmarks of IQM's 20-Qubit Quantum Computer [56.435136806763055]
  IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. The focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.
  arXiv  Detail & Related papers  (2024-08-22T14:26:10Z)
• Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines [0.0]
  This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. We scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines operational on quantum hardware over the Iris dataset.
  arXiv  Detail & Related papers  (2023-10-17T01:06:59Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC [5.747925022035578]
  We have pioneered employing a quantum machine learning algorithm to study the $e+e- rightarrow ZH$ process at the Circular Electron-Positron Collider (CEPC) Using 6 qubits on quantum computer simulators, we optimised the QSVM- Kernel algorithm and obtained a classification performance similar to the classical support-vector machine algorithm. Our study shows that state-of-the-art quantum computing technologies could be utilised by particle physics.
  arXiv  Detail & Related papers  (2022-09-26T15:46:30Z)
• Towards understanding the power of quantum kernels in the NISQ era [79.8341515283403]
  We show that the advantage of quantum kernels is vanished for large size datasets, few number of measurements, and large system noise. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
  arXiv  Detail & Related papers  (2021-03-31T02:41:36Z)
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays [0.27342795342528275]
  We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703.
  arXiv  Detail & Related papers  (2021-03-23T02:09:05Z)
• 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 [6.56216604465389]
  Quantum machine learning could become a powerful tool for data analysis in high energy physics. We employ the quantum variational classifier method in two recent LHC flagship physics analyses. We foresee the usage of quantum machine learning in future high-luminosity LHC physics analyses.
  arXiv  Detail & Related papers  (2020-12-21T18:39:36Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)
• Quantum simulation of open quantum systems in heavy-ion collisions [0.0]
  We present a framework to simulate the dynamics of hard probes such as heavy quarks or jets in a hot, strongly-coupled quark-gluon plasma (QGP) on a quantum computer. Our work demonstrates the feasibility of simulating open quantum systems on current and near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-07T18:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.