# 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

@article{Wu2021ApplicationOQ, 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}, year={2021} }

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…

## 22 Citations

Hybrid Quantum-Classical Graph Convolutional Network

- Computer Science, PhysicsArXiv
- 2021

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
- 2021

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

- Computer Sciencenpj Quantum Information
- 2021

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

- Computer ScienceEntropy
- 2021

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

- PhysicsArXiv
- 2021

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

- Physics, Computer SciencePhysical Review D
- 2022

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.

Challenges and opportunities in quantum machine learning for high-energy physics

- PhysicsNature Reviews Physics
- 2022

Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data

- Computer Science, PhysicsArXiv
- 2022

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

- PhysicsActa Physica Polonica B Proceedings Supplement
- 2022

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

- Computer Science, Physics
- 2022

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.

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