Reservoir Computing via Quantum Recurrent Neural Networks

TL;DR

This paper introduces a quantum reservoir computing framework for recurrent neural networks that reduces training complexity and enhances efficiency, enabling faster learning and better suitability for NISQ quantum hardware.

Contribution

It proposes a novel QRNN-RC model that treats quantum recurrent networks as dynamical systems with only the final layer trained, significantly reducing training complexity.

Findings

QRNN-RC achieves comparable results to fully trained QRNNs in function approximation and time series tasks.

The quantum reservoir approach trains faster and requires fewer epochs than classical RNNs.

The method is more efficient for NISQ hardware, enabling practical quantum sequential modeling.

Abstract

Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers.