  Our primary objective is to conduct a brief survey of various classical and
quantum neural net sequence models, which includes self-attention and recurrent
neural networks, with a focus on recent quantum approaches proposed to work
with near-term quantum devices, while exploring some basic enhancements for
these quantum models. We re-implement a key representative set of these
existing methods, adapting an image classification approach using quantum
self-attention to create a quantum hybrid transformer that works for text and
image classification, and applying quantum self-attention and quantum recurrent
neural networks to natural language processing tasks. We also explore different
encoding techniques and introduce positional encoding into quantum
self-attention neural networks leading to improved accuracy and faster
convergence in text and image classification experiments. This paper also
performs a comparative analysis of classical self-attention models and their
quantum counterparts, helping shed light on the differences in these models and
their performance.
