  In the past decade, the field of quantum machine learning has drawn
significant attention due to the prospect of bringing genuine computational
advantages to now widespread algorithmic methods. However, not all domains of
machine learning have benefited equally from quantum enhancements. Notably,
deep learning and reinforcement learning, despite their tremendous success in
the classical domain, both individually and combined, remain relatively
unaddressed by the quantum community. Arguably, one reason behind this is the
systematic use in these domains of models and methods without prominent
computational bottlenecks, leaving little room for quantum improvements. In
this work, we study the state-of-the-art neural-network approaches for
reinforcement learning with quantum enhancements in mind. We demonstrate the
substantial learning advantage that models with a sampling bottleneck can
provide over conventional neural network architectures in complex learning
environments. These so-called energy-based models, like deep energy-based
reinforcement learning, and deep projective simulation that we also introduce
in this work, effectively allow to trade off learning performance for
efficiency of computation. To alleviate the additional computational costs, we
propose to leverage future and near-term quantum algorithms, resulting in
overall more advantageous learning algorithms. This is achieved using
cutting-edge and new quantum computing machinery to speed-up classical sampling
methods and by employing generalized models to gain an additional quantum
advantage.
