In 2016,Mario Krenn, Anton Zeilinger and colleagues at the IQOQI-Vienna of the Austrian Academy of Sciences (ÖAW) and at the University of Vienna have developed the algorithm Melvin, which can automatically design new quantum experiments for which the human scientists had no answer until then [1]. Since then, several of these experiments have been successfully implemented in the laboratories of Zeilinger group [2-4]. Also, the unintuitive solutions of the algorithm have led to new ideas and connections in quantum physics [5,6].

In the more recent study “Active learning machine learns to create new quantum experiments” [7] the Viennese physicists have joined forces with the group of Hans Briegel from the University of Innsbruck, engaged in research at the boundary between quantum physics and artificial intelligence, to expand Melvin's ability with Artificial Intelligence. This research has now been chosen to represent scientific excellence and originality in the class of Physical and Mathematical Sciences of the Cozzarelli prize. The prize was established in 2005 and later named to honor late PNAS Editor-in-Chief Nicholas R. Cozzarelli.

In this work, the Austrian collaboration has applied the knowledge of the field of artificial intelligence directly to questions in quantum experiments, to improve the Melvin algorithm. This was achieved with the insight that the search for experiments can actually be seen as navigating a labyrinth - a prominent problem in artificial intelligence research. With the help of a Reinforcement Learning Algorithm (Projective Simulation) developed in Innsbruck, it was not only possible to find quantum experiments for specific questions, but even to optimize them autonomously (without human predefined rules). Moreover, the new improved Melvin algorithm - after training - finds more heterogeneous solutions than the original algorithm, because it able to search the labyrinth of optical elements more efficiently.

After training, the new Melvin algorithm has found some optical configurations for experiments that human researchers have long used and considered essential, proving that the algorithm is able to design relevant experiments. By applying it to other less well-researched problems, scientists will be able to find possible new experimental solutions and new physical insights.

Publication in PNAS “Active learning machine learns to create new quantum experiments”, A.A. Melnikov, H.P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, H.J. Briegel (2018).

[1] M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz and A. Zeilinger, Automated search for new quantum experiments. Physical Review Letters 116, 090405 (2016).

[2] M. Malik, M. Erhard, M. Huber, M. Krenn, R. Fickler, A. Zeilinger, Multi-photon entanglement in high dimensions. Nature Photonics 10 (4), 248 (2016).

[3] A. Babazadeh, M. Erhard, F. Wang, M. Malik, R. Nouroozi, M. Krenn, A. Zeilinger, High-Dimensional Single-Photon Quantum Gates: Concepts and Experiments. Physical Review Letters 119 (18), 180510 (2017).

[4] M. Erhard, M. Malik, M. Krenn, A. Zeilinger, Experimental GHZ entanglement beyond qubits, arXiv:1708.03881 (2017).

[5] M. Krenn, A. Hochrainer, M. Lahiri, A. Zeilinger, Entanglement by Path Identity, Physical Review Letters 118 (8), 080401 (2017).

[6] M. Krenn, X. Gu, A. Zeilinger, Quantum Experiments and Graphs: Multiparty States as coherent superpositions of Perfect Matchings, Physical Review Letters 119 (24), 240403 (2017).

[7] A.A. Melnikov, H.P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, H.J. Briegel, Active learning machine learns to create new quantum experiments, Proceedings of the National Academy of Sciences 115 (6), 1221-1226 (2018).