Quantum machine learning for particle physics using a variational quantum classifier

Author:

Blance AndrewORCID,Spannowsky Michael

Abstract

Abstract 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.

Publisher

Springer Science and Business Media LLC

Subject

Nuclear and High Energy Physics

Reference69 articles.

1. D. Silver et al., Mastering the game of go without human knowledge, Nature 550 (2017) 354.

2. D. Silver et al., A general reinforcement learning algorithm that masters chess, shogi, and go through self-play, Science 362 (2018) 1140.

3. K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.

4. A. Butter et al., The Machine Learning Landscape of Top Taggers, SciPost Phys. 7 (2019) 014 [arXiv:1902.09914] [INSPIRE].

5. I. Sutskever, O. Vinyals and Q. Le, Sequence to sequence learning with neural networks, Adv. Neural Inf. Process. Syst. 4 (2014).

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