AI Agents designed by Physicists automatically design quantum experiments.

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Physicists from Innsbruck and Vienna have presented an Artificially Intelligent (AI) agent that automatically designs quantum experiments.

Starting with an empty laboratory table for photonic quantum experiments. The artificial agent then tries to develop new experiments by virtually placing mirrors, prisms or beam splitters on the table. If its actions lead to a meaningful result, the agent has a higher chance to do similar sequence of actions in the future. This is known as a reinforcement learning strategy.

Scientists from Innsbruck have teamed up with Viennese colleagues in the group of Anton Zeilinger, who had earlier experimented the usefulness of automated procedures in the design of quantum experiments with a search algorithm called Melvin. Some of these computer-inspired experiments have already been performed in the lab of Zeilinger. Quantum experiments are an ideal environment to test the applicability of AI to research. Therefore, they used the projective simulation model to investigate the potential of artificial learning agents in this test-bed. The study was published in Proceedings of the National Academy of Sciences. The work was funded by the Austrian Science Fund FWF and the Templeton World Charity Foundation.

“Reinforcement learning is what distinguishes our model from the previously studied automated search, which is governed by unbiased random search,” says Alexey Melnikov from the Department of Theoretical Physics at the University of Innsbruck. “The artificial agent performs tens of thousands of experiments on the virtual laboratory table. When we analyzed the memory of the machine, we discovered that certain structures have developed,” explains his colleague Hendrik Poulsen Nautrup.

In the group of Innsbruck physicists Hans Briegel, researchers wonder to what limits the machines can carry out research on an autonomous mode. To serve this purpose, they use the projective simulation model for artificial intelligence, developed by the group, to make a machine capable of learning and acting creatively. The memory of this autonomous machine stores many fragments of experience, which are linked together. The machine builds up and adapts its memories while learning from both successful and unsuccessful experience.

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