Quantum Long Short-Term Memory

• URL: http://arxiv.org/abs/2009.01783v1
• Date: Thu, 3 Sep 2020 16:41:09 GMT
• Title: Quantum Long Short-Term Memory
• Authors: Samuel Yen-Chi Chen, Shinjae Yoo, and Yao-Lung L. Fang
• Abstract summary: Long short-term memory (LSTM) is a recurrent neural network (RNN) for sequence and temporal dependency data modeling. We propose a hybrid quantum-classical model of LSTM, which we dub QLSTM. Our work paves the way toward implementing machine learning algorithms for sequence modeling on noisy intermediate-scale quantum (NISQ) devices.
• Score: 3.675884635364471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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