Quantum Kernel-Based Long Short-term Memory

TL;DR

The paper introduces QK-LSTM, a quantum-enhanced LSTM model that embeds data into quantum feature space, reducing parameters while maintaining accuracy, suitable for resource-limited quantum devices.

Contribution

It presents the first integration of quantum kernel functions into LSTM architectures, enabling efficient sequence modeling with fewer parameters.

Findings

Achieves comparable performance to classical LSTM

Reduces model complexity and parameter count

Demonstrates effective convergence and robustness

Abstract

The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing.