  Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN)
for sequence and temporal dependency data modeling and its effectiveness has
been extensively established. In this work, we propose a hybrid
quantum-classical model of LSTM, which we dub QLSTM. We demonstrate that the
proposed model successfully learns several kinds of temporal data. In
particular, we show that for certain testing cases, this quantum version of
LSTM converges faster, or equivalently, reaches a better accuracy, than its
classical counterpart. Due to the variational nature of our approach, the
requirements on qubit counts and circuit depth are eased, and our work thus
paves the way toward implementing machine learning algorithms for sequence
modeling on noisy intermediate-scale quantum (NISQ) devices.
