AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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2024

Schmidt, P.; Arlt, S.; Ruiz-Gonzalez, C.; Gu, X.; Rodríguez, C.; Krenn, M.

Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics Journal Article

In: Machine Learning: Science and Technology, vol. 5, no. 3, 2024, ISSN: 26322153 (ISSN).

Abstract | Links | BibTeX | Tags: 3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment

@article{schmidt_virtual_2024,

title = {Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics},

author = {P. Schmidt and S. Arlt and C. Ruiz-Gonzalez and X. Gu and C. Rodríguez and M. Krenn},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201265211&doi=10.1088%2f2632-2153%2fad5fdb&partnerID=40&md5=3a6af280ba0ac81507ade10f5dd1efb3},

doi = {10.1088/2632-2153/ad5fdb},

issn = {26322153 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Machine Learning: Science and Technology},

volume = {5},

number = {3},

abstract = {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 to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields. © 2024 The Author(s). Published by IOP Publishing Ltd.},

keywords = {3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment},

pubstate = {published},

tppubtype = {article}

}