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Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Mario Krenn1,2,3,4,*, Jakob S. Kottmann1,2, Nora Tischler5,6, and Alán Aspuru-Guzik1,2,3,7,†

  • 1Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Canada
  • 2Department of Computer Science, University of Toronto, Canada
  • 3Vector Institute for Artificial Intelligence, Toronto, Canada
  • 4Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
  • 5Centre for Quantum Computation and Communication Technology (Australian Research Council), Centre for Quantum Dynamics, Griffith University, Brisbane, Australia
  • 6Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Germany
  • 7Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada

  • *mario.krenn@utoronto.ca
  • †alan@aspuru.com
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Phys. Rev. X 11, 031044 – Published 26 August, 2021

DOI: https://doi.org/10.1103/PhysRevX.11.031044

Abstract

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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In recent years, a plethora of applications of artificial intelligence in various fields of physics have emerged. They are used to identify new exoplanets in astronomy, flag interesting events in high-energy particle colliders, and optimize quantum computing devices, to name just a few. While these applications have great potential for accelerating future scientific and technological progress, it is still unknown how artificial intelligence techniques can help researchers acquire new scientific understanding for unsolved questions. We introduce a method that can solve open questions in quantum optics design.

Our technique presents solutions in such a way that human scientists can interpret them and understand their conceptual cores, thanks to a physics-inspired interpretable representation of quantum optics and discrete optimization. In demonstrating this method, we discover a new quantum interference concept and the first experimental blueprints for several essential quantum states. These include the first feasible method to generate a vital resource state for photonic quantum computing. In all cases, the solutions provide conceptual insight that can immediately be generalized.

Our work makes three key contributions: First, the newly discovered designs can readily be implemented in photonics laboratories and will advance progress in photonic quantum technology. Second, the possibility to extract new scientific concepts with the help of computer algorithms will advance fundamental understanding of quantum optics and quantum physics in general. Third, this method will stand as an early example of how artificial intelligence can contribute to science at a conceptual level and can inspire similar efforts in other fields.

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Phys. Rev. X 11, 031044– Published 26 August, 2021
  • Received 23 November 2020
  • Revised 1 July 2021
  • Accepted 1 July 2021
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