# Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics

arXiv: https://arxiv.org/abs/2403.00834 · HTML: https://mlsanity.com/paper/2403.00834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 85 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [Deep learning for molecular design - a review of the state of the art](https://mlsanity.com/paper/1903.04388/) (2019)
- [JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design](https://mlsanity.com/paper/2106.04011/) (2021)
- [Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus](https://mlsanity.com/paper/1902.03178/) (2019)
- [Computer-inspired Quantum Experiments](https://mlsanity.com/paper/2002.09970/) (2020)
- [Immersive and Collaborative Data Visualization Using Virtual Reality Platforms](https://mlsanity.com/paper/1410.7670/) (2014)
- [Sampling molecular conformations and dynamics in a multi-user virtual reality framework](https://mlsanity.com/paper/1801.02884/) (2018)
- [Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework](https://mlsanity.com/paper/1902.01827/) (2019)
- [Visualization in virtual reality: a systematic review](https://mlsanity.com/paper/2203.07616/) (2022)
- [AIive: Interactive Visualization and Sonification of Neural Networks in Virtual Reality](https://mlsanity.com/paper/2109.15193/) (2021)

## Abstract (arXiv)

Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.