Unsupervised quantum circuit learning in high energy physics

Delgado, Andrea; Hamilton, Kathleen E.

doi:10.1103/physrevd.106.096006

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Unsupervised quantum circuit learning in high energy physics

Delgado, Andrea·Hamilton, Kathleen E.

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Abstract

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. Here, in this work, we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS artificial neural networks calorimeters hadron colliders multi-purpose particle detectors quantum algorithms quantum benchmarking quantum computation

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Delgado, Andrea, Hamilton, Kathleen E.. 2022-11-09. Unsupervised quantum circuit learning in high energy physics. https://doi.org/10.1103/physrevd.106.096006

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