  Long Short Term Memory(LSTM) models are the building blocks of many
state-of-the-art natural language processing(NLP) and speech enhancement(SE)
algorithms. However, there are a large number of parameters in an LSTM model.
This usually consumes a large number of resources to train the LSTM model.
Also, LSTM models suffer from computational inefficiency in the inference
phase. Existing model compression methods (e.g., model pruning) can only
discriminate based on the magnitude of model parameters, ignoring the issue of
importance distribution based on the model information. Here we introduce the
MPO decomposition, which describes the local correlation of quantum states in
quantum many-body physics and is used to represent the large model parameter
matrix in a neural network, which can compress the neural network by truncating
the unimportant information in the weight matrix. In this paper, we propose a
matrix product operator(MPO) based neural network architecture to replace the
LSTM model. The effective representation of neural networks by MPO can
effectively reduce the computational consumption of training LSTM models on the
one hand, and speed up the computation in the inference phase of the model on
the other hand. We compare the MPO-LSTM model-based compression model with the
traditional LSTM model with pruning methods on sequence classification,
sequence prediction, and speech enhancement tasks in our experiments. The
experimental results show that our proposed neural network architecture based
on the MPO approach significantly outperforms the pruning approach.
