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… 

A Quantum Graph Neural Network Approach to Particle Track Reconstruction

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

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

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

  • Jing Li
  • Computer Science
    Journal 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

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

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

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

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

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

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.

References

SHOWING 1-10 OF 176 REFERENCES

Event Classification with Quantum Machine Learning in High-Energy Physics

Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples.

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.

A quantum machine learning algorithm based on generative models

A general quantum algorithm for machine learning based on a quantum generative model that is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically.

Quantum circuit learning

A classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which is hybridizing a low-depth quantum circuit and a classical computer for machinelearning, paves the way toward applications of near- term quantum devices for quantum machine learning.

Quantum Boltzmann Machine

This work proposes a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian that allows the QBM efficiently by sampling and discusses the possibility of using quantum annealing processors like D-Wave for QBM training and application.

Quantum embeddings for machine learning

This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning.

Learning quantum state properties with quantum and classical neural networks

Several quantum algorithms to estimate quantum state properties directly without relying on tomography are introduced, which prove the universality of several architectures for the class of properties given as polynomial functionals of a density matrix, and evaluate their performance on some particular properties— purity and entropy—using quantum circuit simulators.

Quantum adiabatic machine learning

This work applies and illustrates this approach to machine learning and anomaly detection via quantum adiabatic evolution in detail to the problem of software verification and validation, with a specific example of the learning phase applied to a problem of interest in flight control systems.

Hierarchical quantum classifiers

It is shown how quantum algorithms based on two tensor network structures can be used to classify both classical and quantum data and may enable classification of two-dimensional images and entangled quantum data more efficiently than is possible with classical methods.

Quantum convolutional neural networks

A quantum circuit-based algorithm inspired by convolutional neural networks is shown to successfully perform quantum phase recognition and devise quantum error correcting codes when applied to arbitrary input quantum states.
...