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

TL;DR

This paper introduces a deep generative model that learns interpretable representations of quantum optics experiments, revealing how experiment structure relates to entanglement and enabling the generation of novel highly entangled states.

Contribution

The study presents the Quantum Optics Variational Auto Encoder (QOVAE), a novel model that interprets and generates quantum optics experiments with entanglement properties, advancing understanding of quantum experiment design.

Findings

QOVAE learns an interpretable latent space of quantum experiments.

QOVAE can generate experiments producing highly entangled states.

The model's internal representations align with quantum physics principles.

Abstract

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are 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 experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable 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. The 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 the 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. The QOVAE and the insights from our investigations can be immediately applied to other physical systems.