一种原型循环单元的量子变分方法
A Quantum Variational Approach to Prototypical Recurrent Unit
- School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气工程与计算机科学学院）
- School of Computer Science, Carleton University（卡尔顿大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出轻量级量子原型循环单元（QPRU），其参数远少于经典及量子循环单元，预测性能与最先进基准相当，兼具可扩展性提升和可训练参数减少的优势。
AI中文摘要：
我们提出了一种轻量级量子原型循环单元（QPRU），其参数数量远少于经典循环架构（如长短期记忆网络LSTM和门控循环单元GRU）以及量子变体（包括量子LSTM（QLSTM）和量子GRU（QGRU））。尽管设计紧凑，QPRU仍实现了有竞争力的预测性能，与最先进的基准模型相当，同时具备重要的结构和实际优势，包括可扩展性增强和可训练参数数量减少。
英文摘要：
We introduce a lightweight Quantum Prototypical Recurrent Unit (QPRU) that requires significantly fewer parameters than both classical recurrent architectures, such as Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU), and quantum variants, including Quantum LSTM (QLSTM) and Quantum GRU (QGRU). Despite its compact design, the QPRU achieves competitive forecasting performance, matching state-of-the-art baselines while offering important structural and practical advantages, including enhanced scalability and a reduced number of trainable parameters.
