4.7 Article

Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

期刊

PHYSICAL REVIEW X
卷 11, 期 3, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevX.11.031044

关键词

Computational Physics; Quantum Physics; Quantum Information

资金

  1. Google Focused Award on Quantum Computing
  2. Industrial Research Chair Program of Canada
  3. U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Quantum Algorithm Teams Program
  4. Canada 150 Research Chair Program, Tata Steel
  5. Office of Naval Research
  6. Austrian Science Fund (FWF) through the Erwin Schrodinger fellowship [J4309]
  7. Griffith University Postdoctoral Fellowship Scheme
  8. Australian Research Council (ARC) Centre of Excellence [CE170100012]
  9. Alexander von Humboldt Foundation
  10. Austrian Science Fund (FWF) [J4309] Funding Source: Austrian Science Fund (FWF)

向作者/读者索取更多资源

Artificial intelligence (AI) has the potential to disrupt physics and science by helping scientists acquire new scientific understanding. The THESEUS algorithm demonstrates how AI can contribute at a conceptual level in physics, specifically in experimental quantum optics, by providing solutions that human scientists can interpret and derive new scientific concepts from.
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.

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