A new experiment at the University of Vienna demonstrated how quantum technology can speed up the learning process of machines. The physicists involved in the work used a quantum processor for single photons as a robot.
The reseach was published in Nature.
There have been major developments recently within the field of quantum computing, and the power of such technologies is continuously being realized. This has led to the technology being used in real-life applications, and now experts want to merge artificial intelligence (AI) and autonomous machines with quantum physics and algorithms.
To achieve this, scientists have been looking into how quantum mechanics can help the learning process of robots, and the other way around. Some of the results have shown how robots can move faster or how quantum experiments can use new learning techniques. Despite moving faster, the robots have still not been able to learn faster, which is needed for the development of complex autonomous machines.
Phillip Walther led an international effort headed by a team of physicists at the university. They were joined by theoreticians from the University of Innsbruck, the Austrian Academy of Sciences, the Leiden University, and the German Aerospace Center.
The collaboration succeeded in experimentally proving the speeding up of a robot’s learning time. The team relied on single photons and an integrated photonic quantum processor designed by MIT. The processor was used as a robot, learning how to route single photons to a predefined direction.
Valeria Saggio is first author of the publication.
“The experiment could show that the learning time is significantly reduced compared to the case where no quantum physics is used,” says Saggio.
The Superposition Principle
The robot can learn by being rewarded for completing the correct move. In a classical world, for example with a left and right turn, only one can be chosen and correct. However, with quantum technology, the robot is able to use the superposition principle, meaning it can take both of those turns at the same time.
Hand Briegel and his team at the University of Innsbruck developed the theoretical ideas on quantum learning agents.
“This key feature enables the implementation of a quantum search algorithm that reduces the number of trials for learning the correct path. As a consequence, an agent that can explore its environment in superposition will learn significantly faster than its classical counterpart,” says Briegel.
According to Walther, “We are just at the beginning of understanding the possibilities of quantum artificial intelligence and thus every new experimental result contributes to the development of this field, which is currently seen as one of the most fertile areas for quantum computing.”
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