提出递归量子LSTM，提升时序数据处理能力
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
- 基于元核心递归构建量子循环网络
- 在不同序列长度下验证性能最优架构
- 适合研究量子时序建模与算法优化者
近期量子计算与机器学习的发展推动了用于序列数据处理的量子模型研究。本文提出一种递归量子长短期记忆模型（Recursive QLSTM），通过元核心为基础的递归结构扩展传统QLSTM。我们在不同输入序列长度、元核心设计和递归规则下进行数值测试，识别出表现最佳的模型架构。针对该优选模型，进一步提供理论论证，解释其递归结构如何改善时间信息传播并提升学习性能。结果表明，Recursive QLSTM为多种长度输入时序数据提供了灵活且高效的量子循环学习框架。
原文摘要 · Abstract (English)
Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing. In this paper, we propose a Recursive Quantum Long Short-Term Memory model, or Recursive QLSTM, which extends QLSTM through metacore-based recursive constructions. We numerically test the model under different input sequence lengths, metacore designs, and recursive rules, and identify the best-performing architecture among these variants. For this selected model, we further provide theoretical arguments explaining why its recursive structure improves temporal information propagation and enhances learning performance. Our results suggest that Recursive QLSTM offers a flexible and effective framework for quantum recurrent learning over input time series of various lengths.
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