  The development of quantum computational techniques has advanced greatly in
recent years, parallel to the advancements in techniques for deep reinforcement
learning. This work explores the potential for quantum computing to facilitate
reinforcement learning problems. Quantum computing approaches offer important
potential improvements in time and space complexity over traditional algorithms
because of its ability to exploit the quantum phenomena of superposition and
entanglement. Specifically, we investigate the use of quantum variational
circuits, a form of quantum machine learning. We present our techniques for
encoding classical data for a quantum variational circuit, we further explore
pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate
both hybrid and pure quantum variational circuit have the ability to solve
reinforcement learning tasks with a smaller parameter space. These comparison
are conducted with two OpenAI Gym environments: CartPole and Blackjack, The
success of this work is indicative of a strong future relationship between
quantum machine learning and deep reinforcement learning.
