用量子核函数增强LSTM，少参数实现高效时序建模
Quantum Kernel-Based Long Short-term Memory
- 将量子核嵌入经典LSTM，映射数据到高维量子空间
- 参数量更少但性能媲美传统LSTM，收敛快且损失低
- 适合边缘计算与资源受限的量子设备部署
将量子计算融入经典机器学习架构，成为提升模型效率与计算能力的有前景方向。本文提出量子核基长短期记忆网络（QK-LSTM），在经典LSTM框架中引入量子核函数，以捕捉序列数据中的复杂非线性模式。通过将输入数据嵌入高维量子特征空间，该模型减少对大规模参数的依赖，在保持序列建模精度的同时实现有效压缩。此量子增强架构展现出高效收敛、稳健损失最小化及模型紧凑性，适用于边缘计算环境与资源受限的量子设备（尤其在NISQ时代）。基准对比表明，QK-LSTM性能与经典LSTM相当，但参数更少，凸显其在自然语言处理及其他需高效时序处理领域推进量子机器学习应用的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。