  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.
