Qubit or Qubot? Quantum Technology May Help Robots Learn Faster

vienna
Artistic impression of the quantum learning concept. (© Rolando Barry, University of Vienna)

More and more, artificial intelligence is becoming part of our modern life by enabling machines to learn useful processes such as speech recognition and digital personal assistants. But, developers are still looking for ways to find ways to make such intelligent machines learn faster. Now, a University of Vienna-led research team report that combining two rapidly emerging areas — AI and quantum technology — they may be able to help with that speed-up, according to a university news release.

An international collaboration of physicists from Austria, Germany, the Netherlands and the USA, have achieved this result by using a quantum processor for single photons as a robot. This work, which contributes to the advancement of quantum artificial intelligence for future applications, is published in the current issue of Nature.

Led by Philip Walther, the team experimentally showed a speed up in the actual robot’s learning time for the first time a speed-up. The team has made use of single photons, the fundamental particles of light, coupled into an integrated photonic quantum processor, which was designed at the Massachusetts Institute of Technology. This processor was used as a robot and for implementing the learning tasks. Here, the robot would learn to route the single photons to a predefined direction.

“The experiment could show that the learning time is significantly reduced compared to the case where no quantum physics is used”, said Valeria Saggio, first author of the publication.

In a nutshell, the experiment can be understood by imagining a robot standing at a crossroad, provided with the task of learning to always take the left turn. The robot learns by obtaining a reward when doing the correct move. Now, if the robot is placed in our usual classical world, then it will try either a left or right turn, and will be rewarded only if the left turn is chosen. In contrast, when the robot exploits quantum technology, the bizarre aspects of quantum physics come into play. The robot can now make use of one of its most famous and peculiar features, the so called superposition principle. This can be intuitively understood by imagining the robot taking the two turns, left and right, at the same time.

“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,” said Hans Briegel, who developed the theoretical ideas on quantum learning agents with his group at the University of Innsbruck.

This experimental demonstration that machine learning can be enhanced by using quantum computing shows promising advantages when combining these two technologies. “We are just at the beginning of understanding the possibilities of quantum artificial intelligence” says Philip Walther, “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”.

The team included experimental physicists from the University of Vienna, together with theoreticians from the University of Innsbruck, the Austrian Academy of Sciences, the Leiden University, and the German Aerospace Center.

You can find the paper here.




Matt Swayne
Matt Swayne
Matt Swayne is a contributor at The Quantum Daily. He focuses on breaking news about quantum discoveries and quantum computing. Matt enjoys working on -- and with -- startups and is currently working on a media studies master's degree, specializing in science communication.

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