Learning interpretable representations of entanglement in quantum optics experiments using deep generative models

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are the core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experiment setups. In a series of computational experiments, we investigate the learned representation of our quantum optics variational autoencoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that QOVAE learns an interpretable representation of quantum optics experiments and the relationship between the experiment structure and entanglement. We show QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. QOVAE and the insights from our investigations can be immediately applied to other physical systems. A variational autoencoder is trained on a dataset of quantum optics experiment configurations and learns an interpretable representation of the relationship between experiment setup and quantum entanglement. The approach can be used to explore new experiment designs with specific, highly entangled states.

Automated summary

This study presents a deep generative model, where a variational autoencoder is trained on a dataset of quantum optics experiment setups, to learn the relationship between experiment setup and quantum entanglement. The model is able to generate new experiments for highly entangled quantum states and interpret the structure of latent space.