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2021

Anomaly detection in high-energy physics using a quantum autoencoder

Ngairangbam, Vishal S., Spannowsky, Michael, Takeuchi, Michihisa

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The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics.

Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions.
We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC.
For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently.

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