New research provides a Rosetta Stone that translates the language of reinforcement learning to the quantum realm. It tackles sticky questions like what it means for a quantum agent to learn and how the history of a quantum agent’s interaction with its environment can be captured in a meaningful way. It also shows how a standard algorithm in the quantum toolkit can help agents learn faster in settings where an early stroke of luck can make a big difference—like when learning how to navigate a maze.
Future research could investigate whether a quantum computer, with the added help of a quantum agent, could learn about its own noisy environment fast enough to change the way it reacts to errors. The work may also shed light on one of the deepest questions in physics: How does the everyday world arise from interactions that are, at the microscopic level, described by quantum mechanics? Studying how quantum agents interact with their quantum environments could add to the understanding of this lingering mystery.
To date, much research in the emerging field of quantum machine learning has attacked choke points in ordinary machine learning tasks, focusing, for example, on how to use quantum computers to speed up image recognition. But Vedran Dunjko and Hans Briegel at the University of Innsbruck, in collaboration with JQI Fellow Jake Taylor, have taken a broader view. Rather than focusing on speeding up subroutines for specific tasks, the researchers have introduced an approach to quantum machine learning that unifies much of the prior work and extends it to problems that received little attention before. They also showed how to increase learning performance for a large group of problems.
SOURCES – Physical Review Letters, Arxiv
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