Quantum anomaly detection in the latent space of proton collision events
at the LHC
- URL: http://arxiv.org/abs/2301.10780v1
- Date: Wed, 25 Jan 2023 19:00:01 GMT
- Title: Quantum anomaly detection in the latent space of proton collision events
at the LHC
- Authors: Kinga Anna Wo\'zniak, Vasilis Belis, Ema Puljak, Panagiotis
Barkoutsos, G\"unther Dissertori, Michele Grossi, Maurizio Pierini, Florentin
Reiter, Ivano Tavernelli, Sofia Vallecorsa
- Abstract summary: We propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms.
For kernel-based anomaly detection, we identify a regime where the quantum model significantly outperforms its classical counterpart.
We demonstrate that the observed consistent performance advantage is related to the inherent quantum properties of the circuit used.
- Score: 1.0480625205078853
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a new strategy for anomaly detection at the LHC based on
unsupervised quantum machine learning algorithms. To accommodate the
constraints on the problem size dictated by the limitations of current quantum
hardware we develop a classical convolutional autoencoder. The designed quantum
anomaly detection models, namely an unsupervised kernel machine and two
clustering algorithms, are trained to find new-physics events in the latent
representation of LHC data produced by the autoencoder. The performance of the
quantum algorithms is benchmarked against classical counterparts on different
new-physics scenarios and its dependence on the dimensionality of the latent
space and the size of the training dataset is studied. For kernel-based anomaly
detection, we identify a regime where the quantum model significantly
outperforms its classical counterpart. An instance of the kernel machine is
implemented on a quantum computer to verify its suitability for available
hardware. We demonstrate that the observed consistent performance advantage is
related to the inherent quantum properties of the circuit used.
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