Quantum-Enhanced Recurrent Neural Networks via Variational Quantum Gating for Battery State of Health Prediction

TL;DR

This paper introduces QLSTM, a quantum-enhanced recurrent neural network that embeds variational quantum circuits into LSTM gates, significantly improving battery state-of-health prediction accuracy over classical models.

Contribution

The work presents a novel quantum-enhanced recurrent framework, embedding variational quantum circuits into LSTM gates, demonstrating improved accuracy and robustness in battery SOH prediction.

Findings

QLSTM outperforms classical LSTM in predictive accuracy by about 20%.

Quantum gating improves model robustness and reduces mean absolute error.

Model performance depends on a balance between quantum circuit complexity and noise robustness.

Abstract

Accurate state-of-health (SOH) estimation for lithium-ion batteries remains a challenging problem due to complex electrochemical degradation mechanisms and long-range temporal dependencies. In this work, we propose a quantum-enhanced recurrent framework, termed QLSTM, in which variational quantum circuits are directly embedded into the gating mechanisms of long short-term memory networks. By replacing classical affine transformations with parameterized unitary operations, the proposed model introduces structured nonlinear transformations into the recurrent state-transition process. Extensive experiments on multiple benchmark battery datasets demonstrate that QLSTM consistently outperforms classical sequence models in both predictive accuracy and robustness, achieving significant reductions in mean absolute error (MAE), with improvements on the order of 20% compared with classical LSTM baselines. Ablation studies further confirm that these improvements arise primarily from quantum-enhanced gating rather than input-level transformations. Additional analyses on qubit scaling and noise robustness reveal that model performance is governed by a balance between expressive capacity and trainability. These results provide empirical evidence that embedding quantum computational primitives within recurrent architectures offers a structurally grounded approach to improving sequence modeling capability. The proposed framework establishes a new design paradigm for integrating quantum operators into temporal learning models, with potential applications in complex dynamical system prediction tasks.