Quantum Computing presents an interesting paradigm where it can possibly
offer certain improvements and additions to a classical network while
training. This method is particularly prevalent in the current Noisy
Intermediate-Scale Quantum era, where we can test these theories using
libraries such as Pennylane in conjunction with robust ML frameworks
such as TensorFlow. This paper presents a proof-of-concept for the same,
using a hybrid quantum-classical model to solve a text classification
problem on the IMDB Movie Sentiment Dataset. These hybrid models utilize
precalculated embeddings and dense layers alongside a variational
quantum circuit layer. We created 4 such models, utilizing various kinds
of embeddings, namely NNLM-128, NNLM-50, Swivel and USE, using TFHub and
Pennylane. We also trained classical versions of these models, without
the variational quantum layer to evaluate the performances. All models
were trained on the same data, keeping the parameters constant.