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Journal Article

Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models


Krenn,  M.
Department of Computer Science, University of Toronto;
Vector Institute for Artificial Intelligence;
Department of Chemistry, University of Toronto;
Institute of Advanced Research in Artificial Intelligence (IARAI);
Krenn Research Group, Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;

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Flam-Shepherd, D., Wu, T., Gu, X., Cervera-Lierta, A., Krenn, M., & Aspuru-Guzik, A. (2022). Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models. Nature Machine Intelligence, s42256-022-00493-5. doi:10.1038/s42256-022-00493-5.

Cite as: https://hdl.handle.net/21.11116/0000-0009-71E4-6
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which is a core property of numerous future quantum technologies. The complex relationship between a quantum experiment's structure and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present the first deep generative model of quantum optics experiments where a variational autoencoder (QOVAE) is trained on a dataset of experimental setups. In a series of computational experiments, we investigate the learned representation of the QOVAE and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an intrepretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. Importantly, we are able to fully interpret how the QOVAE structures its latent space, finding curious patterns that we can entirely explain in terms of quantum physics. The results demonstrate how we can successfully use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems throughout fundamental scientific research.