Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts....

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Autores principales:	Mario Krenn, Jakob S. Kottmann, Nora Tischler, Alán Aspuru-Guzik
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/a34eeb74fc0845a697308b7fa2729cdc
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spelling	oai:doaj.org-article:a34eeb74fc0845a697308b7fa2729cdc2021-12-02T16:33:42ZConceptual Understanding through Efficient Automated Design of Quantum Optical Experiments10.1103/PhysRevX.11.0310442160-3308https://doaj.org/article/a34eeb74fc0845a697308b7fa2729cdc2021-08-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.031044http://doi.org/10.1103/PhysRevX.11.031044https://doaj.org/toc/2160-3308Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.Mario KrennJakob S. KottmannNora TischlerAlán Aspuru-GuzikAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 3, p 031044 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Mario Krenn Jakob S. Kottmann Nora Tischler Alán Aspuru-Guzik Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
description	Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.
format	article
author	Mario Krenn Jakob S. Kottmann Nora Tischler Alán Aspuru-Guzik
author_facet	Mario Krenn Jakob S. Kottmann Nora Tischler Alán Aspuru-Guzik
author_sort	Mario Krenn
title	Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
title_short	Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
title_full	Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
title_fullStr	Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
title_full_unstemmed	Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
title_sort	conceptual understanding through efficient automated design of quantum optical experiments
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/a34eeb74fc0845a697308b7fa2729cdc
work_keys_str_mv	AT mariokrenn conceptualunderstandingthroughefficientautomateddesignofquantumopticalexperiments AT jakobskottmann conceptualunderstandingthroughefficientautomateddesignofquantumopticalexperiments AT noratischler conceptualunderstandingthroughefficientautomateddesignofquantumopticalexperiments AT alanaspuruguzik conceptualunderstandingthroughefficientautomateddesignofquantumopticalexperiments
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Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

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