  Quantum computing holds great potential for advancing the limitations of
machine learning algorithms to handle higher dimensions of data and reduce
overall training parameters in deep learning (DL) models. This study uses a
trainable variational quantum circuit (VQC) on a gate-based quantum computing
model to investigate the potential for quantum benefit in a model-free
reinforcement learning problem. Through a comprehensive investigation and
evaluation of the current model and capabilities of quantum computers, we
designed and trained a novel hybrid quantum neural network based on the latest
Qiskit and PyTorch framework. We compared its performance with a full-classical
CNN with and without an incorporated VQC. Our research provides insights into
the potential of deep quantum learning to solve a maze problem and,
potentially, other reinforcement learning problems. We conclude that
reinforcement learning problems can be practical with reasonable training
epochs. Moreover, a comparative study of full-classical and hybrid quantum
neural networks is discussed to understand these two approaches' performance,
advantages, and disadvantages to deep-Q learning problems, especially on
larger-scale maze problems larger than 4x4.
