Quantum Kernel-Based Long Short-term Memory
This work addresses the need for efficient sequence modeling in resource-limited settings like edge computing and NISQ-era quantum devices, though it appears incremental as it builds on existing LSTM frameworks.
The paper tackles the problem of enhancing classical LSTM models for sequential data by integrating quantum kernel functions, resulting in a QK-LSTM network that achieves performance comparable to classical LSTMs with fewer parameters.
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.