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More recently, research has suggested that quantum effects could offer similar advantages for the emerging field of quantum machine learning (a subfield of artificial intelligence), leading to more intelligent machines that learn quickly and efficiently by interacting with their environments.
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| Physicists have shown
that quantum effects have the potential to significantly improve a
variety of interactive learning tasks in machine learning. This figure
shows a tested agent-environment interaction. Credit: Dunjko et al. ©2016 American Physical Society |
In a new study published in Physical Review Letters, Vedran Dunjko and coauthors have added to this research, showing that quantum effects can likely offer significant benefits to machine learning.
"The progress in machine learning critically relies on processing power," Dunjko, a physicist at the University of Innsbruck in Austria, told Phys.org. "Moreover, the type of underlying information processing that many aspects of machine learning rely upon is particularly amenable to quantum enhancements. As quantum technologies emerge, quantum machine learning will play an instrumental role in our society—including deepening our understanding of climate change, assisting in the development of new medicine and therapies, and also in settings relying on learning through interaction, which is vital in automated cars and smart factories."
In the new study, the researchers' main result is that quantum effects can help improve reinforcement learning, which is one of the three main branches of machine learning. They showed that quantum effects have the potential to provide quadratic improvements in learning efficiency, as well as exponential improvements in performance for short periods of time when compared to classical techniques for a wide class of learning problems.
While other research groups have previously shown that quantum effects can offer improvements for the other two main branches of machine learning (supervised and unsupervised learning), reinforcement learning has not been as widely investigated from a quantum perspective.
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Additional resources
Vedran Dunjko, Jacob M. Taylor, and Hans J. Briegel. "Quantum-Enhanced Machined Learning." Physical Review Letters. DOI: 10.1103/PhysRevLett.117.130501
Source: Phys.Org
