Quantum Recurrent Neural Networks for Sequential Learning

TL;DR

This paper introduces a new quantum recurrent neural network (QRNN) model designed for near-term quantum devices, demonstrating superior performance over classical RNNs and other QNNs on various sequential data tasks.

Contribution

The paper proposes a canonical QRNN model with hardware-efficient quantum recurrent blocks, reducing coherence time requirements and enhancing practicality on NISQ devices.

Findings

QRNN outperforms classical RNNs in prediction accuracy

QRNN effectively models temporal sequence data

The model is feasible on current quantum hardware

Abstract

Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent blocks (QRBs) are constructed in the hardware-efficient way, and the QRNN is built by stacking the QRBs in a staggered way that can greatly reduce the algorithm's requirement with regard to the coherent time of quantum devices. That is, our QRNN is much more accessible on NISQ devices. Furthermore, the performance of the present QRNN model is verified concretely using three different kinds of classical sequential data, i.e., meteorological indicators, stock price, and text categorization. The numerical experiments show that our QRNN achieves much better performance in prediction (classification) accuracy against the classical RNN and state-of-the-art QNN models for sequential learning, and can predict the changing details of temporal sequence data. The practical circuit structure and superior performance indicate that the present QRNN is a promising learning model to find quantum advantageous applications in the near term.