Quantum machine learning for particle physics using a variational quantum classifier

Journal of High Energy Physics

Michael Spannowsky¹  Andrew Blance² 

[1] IPPP, Department of Physics, Durham University, DH1 3LE, Durham, U.K.;IPPP, Department of Physics, Durham University, DH1 3LE, Durham, U.K.;Institute for Data Science, Durham University, DH1 3LE, Durham, U.K.;

关键词: Beyond Standard Model;    Hadron-Hadron scattering (experiments);    Jet physics;    Particle correlations and fluctuations;    Top physics;   

DOI  :  10.1007/JHEP02(2021)212

来源: Springer

【 摘 要 】

Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.

【 授权许可】

CC BY