Improving Quantum Recurrent Neural Networks with Amplitude Encoding

TL;DR

This paper enhances quantum recurrent neural networks by improving amplitude encoding techniques and circuit architecture, leading to better performance and resource efficiency in time series forecasting.

Contribution

It introduces a pre-processing method for amplitude encoding and a new, shallower circuit architecture, advancing QRNN design and practical applicability.

Findings

Improved generalization on real-world datasets.

Reduced circuit depth without loss of performance.

Enhanced quantum resource efficiency.

Abstract

Quantum machine learning holds promise for advancing time series forecasting. The Quantum Recurrent Neural Network (QRNN), inspired by classical RNNs, encodes temporal data into quantum states that are periodically input into a quantum circuit. While prior QRNN work has predominantly used angle encoding, alternative encoding strategies like amplitude encoding remain underexplored due to their high computational complexity. In this paper, we evaluate and improve amplitude-based QRNNs using EnQode, a recently introduced method for approximate amplitude encoding. We propose a simple pre-processing technique that augments amplitude encoded inputs with their pre-normalized magnitudes, leading to improved generalization on two real world data sets. Additionally, we introduce a novel circuit architecture for the QRNN that is mathematically equivalent to the original model but achieves a substantial reduction in circuit depth. Together, these contributions demonstrate practical improvements to QRNN design in both model performance and quantum resource efficiency.