Hybrid Quantum-Classical Neural Networks for Text Classification

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.