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Journal of Physics Conference Series

Quantum machine learning in the latent space of high energy physics events

K. A. Wozniak, Vasilis Belis, Maurizio Pierini B, Sofia Vallecorsa, G. Dissertori, Panagiotis Kl. Barkoutsos, Ivano Tavernelli

Published Feb 1, 2023• 3 citations
Publisher

Abstract

Abstract We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.

DOI
10.1088/1742-6596/2438/1/012115

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Abstract We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.
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This study explores the potential of unsupervised anomaly detection for identifying physics beyond the standard model that may appear at proton collisions at the Large Hadron Collider. We introduce a novel quantum autoencoder circuit ansatz that is specifically designed for this task and demonstrates superior performance compared to previous approaches. To assess its robustness, we evaluate the quantum autoencoder on various types of new physics 'signal' events and varying problem sizes. Additionally, we develop classical autoencoders that outperform previously proposed quantum autoencoders but remain outpaced by the new quantum ansatz, despite its significantly reduced number of trainable parameters. Finally, we investigate the properties of quantum autoencoder circuits, focusing on entanglement and magic. We introduce a novel metric in the context of parameterised quantum circuits, stabiliser 2-Rényi entropy to quantify magic, along with the previously studied Meyer-Wallach measure for entanglement. Intriguingly, both metrics decreased throughout the training process along with the decrease in the loss function. This appears to suggest that models preferentially learn parameters that reduce (but not minimise) these metrics. This study highlights the potential utility of quantum autoencoders in searching for physics beyond the standard model at the Large Hadron Collider and opens exciting avenues for further research into the role of entanglement and magic in quantum machine learning more generally.
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