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arXiv:1811.08676 · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

Advances in quantum reinforcement learning

Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel

Published Oct 1, 2017• 81 citations
arXivHTMLPDF

Abstract

In recent times, there has been much interest in quantum enhancements of machine learning, specifically in the context of data mining and analysis. Reinforcement learning, an interactive form of learning, is, in turn, vital in artificial intelligence-type applications. Also in this case, quantum mechanics was shown to be useful, in certain instances. Here, we elucidate these results, and show that quantum enhancements can be achieved in a new setting: the setting of learning models which learn how to improve themselves - that is, those that metalearn. While not all learning models meta-learn, all non-trivial models have the potential of being "lifted", enhanced, to meta-learning models. Our results show that also such models can be quantum-enhanced to make even better learners. In parallel, we address one of the bottlenecks of current quantum reinforcement learning approaches: the need for so-called oracularized variants of task environments. Here we elaborate on a method which realizes these variants, with minimal changes in the setting, and with no corruption of the operative specification of the environments. This result may be important in near-term experimental demonstrations of quantum reinforcement learning.

DOI
10.1109/smc.2017.8122616

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A vital, transformative and impactful field on our society is levied on us by the incoming quantum computation and information technologies. The undergoing research on the interaction of fields of quantum computation with the machine learning and artificial intelligence is witnessing significant investigations. Evidently there has been a major demonstration of quantum enhancements in interactive learning tasks and the optimization of quantum experiments using ML sowing the seeds of interdisciplinary activities to improve their influence in our hugely populated data intensive digital world. The quantum mechanical world is evidently taking shapes beyond theoretical analysis with the aid of AI previously uncharted. This composite review, to the best of our knowledge, makes an attempt to explore the recent headway for actuating AI and ML in the quantum domain.
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