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

  title={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},
  author={Sau Lan Wu and Jay Chan and Wen Guan and Shaojun Sun and Alex Zeng Wang and Chengda Zhou and Miron Livny and Federico Carminati and Alberto Di Meglio and Andy C. Y. Li and J. Lykken and Panagiotis Spentzouris and Samuel Yen-Chi Chen and Shinjae Yoo and Tzu-Chieh Wei},
  journal={Journal of Physics G: Nuclear and Particle Physics},
  • S. Wu, J. Chan, T. Wei
  • Published 21 December 2020
  • Physics
  • Journal of Physics G: Nuclear and Particle Physics
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 gatemodel quantum computing systems, we employ the quantum variational… 

Figures from this paper

Hybrid Quantum-Classical Graph Convolutional Network
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data that demonstrates an advantage over classical multilayer perceptron and Convolutional neural networks in the aspect of number of parameters.
Investigating Quantum Speedup for Track Reconstruction: Classical and Quantum Computational Complexity Analysis
Physics of Information and Quantum Technologies Group, Instituto de Telecomunicações, Portugal Instituto Superior Técnico, Universidade de Lisboa, Portugal Department of Mathematics, Clarkson
Machine learning of high dimensional data on a noisy quantum processor
A circuit ansatz is constructed that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and error mitigation specific to the task of computing quantum kernels on near-term hardware is implemented.
Federated Quantum Machine Learning
The distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training, and demonstrates a promising future research direction for scaling and privacy aspects.
A Living Review of Machine Learning for Particle Physics
This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.
Anomaly detection in high-energy physics using a quantum autoencoder
It is shown that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very well, and this performance is reproducible on present quantum devices, shows that quantum aut Koencoders are good candidates for analysing high-energy physics data in future LHC runs.
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
This study provides a comprehensive comparison between classical TNs and TN-inspired quantum circuits in the context of Machine Learning on highly complex, simulated LHC data and shows that Classical TNs require exponentially large bond dimensions and higher Hilbert-space mapping to perform comparably to their quantum counterparts.
From Causal Representation of Multiloop Scattering Amplitudes to Quantum Computing
Quantum computing is a natural advantageous framework for problems where the quantum principles of superposition and entanglement can be exploited. It is currently an approach with great potential in
Psitrum: An Open Source Simulator for Universal Quantum Computers
Simulation of universal quantum computers is presented by introducing Psitrum – a universal gate-model quantum computer simulator implemented on classical hardware that allows to simulate all basic quantum operations and provides variety of visualization tools.


Solving a Higgs optimization problem with quantum annealing for machine learning
This work uses quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model.
Supervised learning with quantum-enhanced feature spaces
Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.
Quantum Machine Learning
This review focuses on the supervised classification quantum algorithm of nearest centroid, presented in [11], which helps to overcome the main bottleneck of the algorithm: calculation of the distances between the vectors in highly dimensional space.
Observation of ttH Production
The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on a combined analysis of proton-proton collision data at center-of-mass energies of √s =
Search for the Higgs Boson Decaying to Two Muons in Proton-Proton Collisions at s =13 TeV
A search for the Higgs boson decaying to two oppositely charged muons is presented using data recorded by the CMS experiment at the CERN LHC in 2016 at a center-of-mass energy s=13 TeV, corresponding
Search for the Dimuon Decay of the Higgs Boson in $pp$ Collisions at $\sqrt{s}$ = 13 TeV with the ATLAS Detector
A search for the dimuon decay of the Higgs boson has been performed using data corresponding to an integrated luminosity of 36.1 fb−1 collected with the ATLAS detector in pp collisions at √ s =13 TeV
PYTHIA 6.2: Physics and manual
The PYTHIA program can be used to generate high-energy-physics `events', i.e. sets of outgoing particles produced in the interactions between two incoming particles. The objective is to provide as
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
A bstractWe discuss the theoretical bases that underpin the automation of the computations of tree-level and next-to-leading order cross sections, of their matching to parton shower simulations, and