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Computer-inspired quantum experiments


The design of new devices and experiments has historically relied on the intuition of human experts. Now, design inspirations from computers are increasingly augmenting the capability of scientists. We briefly overview different fields of physics that rely on computer-inspired designs using a variety of computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we focus specifically on quantum physics. When designing new quantum experiments, there are two challenges: quantum phenomena are unintuitive, and the number of possible configurations of quantum experiments explodes exponentially. These challenges can be overcome by using computer-designed quantum experiments. We focus on the most mature and practical approaches to find new complex quantum experiments, which have subsequently been realized in the lab. These methods rely on a highly efficient topological search, which can inspire new scientific ideas. We review several extensions and alternatives based on various optimization and machine learning techniques. Finally, we discuss what can be learned from the different approaches and outline several future directions.

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Fig. 1: Algorithms for designing quantum experiments.
Fig. 2: Concept of the class III algorithm for computer-inspired experiments, MELVIN.
Fig. 3: The complexity of computer-inspired quantum experiments.
Fig. 4: Concrete example of how a computer-discovered outlier can inspire new ideas, concepts or technology in experimental quantum optics.
Fig. 5: Example of a class IIa algorithm.
Fig. 6: Examples of class I algorithms for computer-inspired quantum experiments using various optimization and machine learning techniques.

Code availability

Example codes both for Wolfram Mathematica and for Python (using SymPy) can be found at


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This work was supported by the Austrian Academy of Sciences (ÖAW), University of Vienna via the project QUESS and the Austrian Science Fund (FWF) with SFB F40 (FOQUS). M.E. acknowledges support from FWF project W 1210-N25 (CoQuS). M.K. acknowledges support from the FWF via the Erwin Schrödinger fellowship number J4309.

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Correspondence to Mario Krenn, Manuel Erhard or Anton Zeilinger.

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Nature Reviews Physics thanks Renato Renner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Krenn, M., Erhard, M. & Zeilinger, A. Computer-inspired quantum experiments. Nat Rev Phys 2, 649–661 (2020).

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