Anomaly detection in high-energy physics using a quantum autoencoder

نویسندگان

چکیده

The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated high-energy physics community to explore model-agnostic data-analysis approaches search physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable background distributions. We study quantum autoencoders variational circuits problem anomaly detection LHC. For a QCD $t\bar{t}$ resonant heavy Higgs signals, we find that simple autoencoder outperforms classical same inputs trains very efficiently. Moreover, this performance is reproducible present devices. This shows good candidates analysing data in future LHC runs.

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ژورنال

عنوان ژورنال: Physical review

سال: 2022

ISSN: ['0556-2813', '1538-4497', '1089-490X']

DOI: https://doi.org/10.1103/physrevd.105.095004