This work investigates how shallow, NISQ-compatible quantum layers can improve temporal representation learning in real-world sequential data. We develop a QLSTM Seq2Seq autoencoder in which a depth-1 variational quantum circuit is embedded inside each recurrent gate, shaping the geometry of the learned latent manifold. Evaluated on fourteen rolling S and P 500 windows from 2022 to 2025, the quantum-enhanced encoder produces smoother trajectories, clearer regime transitions, and more stable, sector-coherent clusters than a classical LSTM baseline. These geometric properties support the use of a Radial Basis Function (RBF) kernel for downstream portfolio allocation, where both RBF-Graph and RBF-DivMom strategies consistently outperform their classical counterparts in risk-adjusted terms. Analysis across periods shows that compressed manifolds favor concentrated allocation, while dispersed manifolds favor diversification, demonstrating that latent geometry serves as a regime indicator. The results highlight a practical role for shallow hybrid quantum and classical layers in NISQ-era sequence modeling, offering a reproducible pathway for improving temporal embeddings in finance and other data-limited, noise-sensitive domains.

翻译：本研究探讨了浅层、与NISQ兼容的量子层如何改进现实世界序列数据中的时序表示学习。我们开发了一种QLSTM序列到序列自编码器，其中在每个循环门内嵌入了一个深度为1的变分量子电路，从而塑造了学习到的潜在流形的几何结构。在2022年至2025年的十四个滚动标普500指数窗口上进行评估，与经典的LSTM基线相比，量子增强编码器产生了更平滑的轨迹、更清晰的状态转换以及更稳定、行业一致性更强的聚类。这些几何特性支持在下游投资组合配置中使用径向基函数（RBF）核，其中RBF-Graph和RBF-DivMom策略在风险调整后的指标上均持续优于其经典对应策略。跨时期分析表明，压缩的流形有利于集中配置，而分散的流形有利于多样化配置，这证明了潜在几何结构可作为状态指示器。研究结果凸显了浅层混合量子-经典层在NISQ时代序列建模中的实际作用，为改进金融及其他数据有限、对噪声敏感领域的时序嵌入提供了一条可复现的路径。