# Quantum machine learning in high energy physics

@article{Guan2021QuantumML, title={Quantum machine learning in high energy physics}, author={Wen Guan and Gabriel N. Perdue and Arthur Pesah and Maria Schuld and Koji Terashi and Sofia Vallecorsa and J. R. Vlimant}, journal={Machine Learning: Science and Technology}, year={2021}, volume={2} }

Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting…

## 41 Citations

### A Quantum Graph Neural Network Approach to Particle Track Reconstruction

- Computer Science
- 2020

An improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model is presented.

### Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC

- Physics, Computer SciencePhysical Review Research
- 2021

This 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.

### Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

- Physics, Computer ScienceQuantum Machine Intelligence
- 2021

This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classicalgraph neural network that benefits from using variational quantum layers.

### Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance

- Computer ScienceJournal of Environmental and Public Health
- 2022

The experimental results show that the machine learning models used to evaluate the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models can meet the needs of practical work and make accurate predictions of outbound data risks.

### Quantum Computing and Simulation Platform for HEP at IHEP

- Physics, Computer ScienceProceedings of International Symposium on Grids & Clouds 2022 — PoS(ISGC2022)
- 2022

With the dramatic growth of experimental data in high-energy physics and the increasing demand for accuracy of physical analysis results, the current classical computing model and computing power can…

### Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

- Computer ScienceIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
- 2022

A novel approach is proposed, which consists in a reformulated Support Vector Regression and leverages Quantum Annealing (QA) and is reframed to a Quadratic Unconstrained Binary Opti-mization (QUBO) problem.

### FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

- Computer Science
- 2022

This paper proposes a novel QCNN training algorithm to optimize feature extraction while using only a ﬁnite number of qubits, which is called fame-variation training (FV-Training).

### Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer

- Computer Science, PhysicsQuantum
- 2022

A new circuit optimizer called AQCEL, which aims to remove redundant controlled operations from controlled gates, depending on initial states of the circuit, and can remove unnecessary qubit controls from multi-controlled gates in polynomial computational resources.

### Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing

- Computer ScienceIEEE Geoscience and Remote Sensing Letters
- 2022

This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on machine learning (ML) algorithms.

### Recent Advances for Quantum Neural Networks in Generative Learning

- Computer Science, PhysicsArXiv
- 2022

This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relation and their fundamental differences.

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