用模块化量子计算解决量子LSTM的扩展难题，提升时序建模能力。
Toward Large-Scale Distributed Quantum Long Short-Term Memory with Modular Quantum Computers
- 将变分量子电路嵌入LSTM单元，实现长程时序依赖捕捉
- 分布式架构将大电路拆解，可在多量子处理器上并行执行
- 在阻尼振子等任务中表现优于经典方法，适合未来量子高性能计算
本文提出一种分布式量子长短期记忆（QLSTM）框架，利用模块化量子计算应对噪声中等规模量子（NISQ）设备的可扩展性挑战。通过将变分量子电路嵌入LSTM单元，QLSTM能够捕捉长程时间依赖；分布式架构将底层变分量子电路（VQC）分割为更小、可管理的子电路，可在量子处理单元网络上执行。我们使用阻尼谐振子和非线性自回归移动平均序列等非平凡基准问题评估该框架，结果表明分布式QLSTM相比经典方法具有更稳定的收敛性和更优的训练动态。本工作凸显了模块化、分布式量子计算架构在大规模序列建模中的潜力，为混合量子-经典解决方案融入先进量子高性能计算（HPC）生态系统奠定了基础。
原文摘要 · Abstract (English)
In this work, we introduce a Distributed Quantum Long Short-Term Memory (QLSTM) framework that leverages modular quantum computing to address scalability challenges on Noisy Intermediate-Scale Quantum (NISQ) devices. By embedding variational quantum circuits into LSTM cells, the QLSTM captures long-range temporal dependencies, while a distributed architecture partitions the underlying Variational Quantum Circuits (VQCs) into smaller, manageable subcircuits that can be executed on a network of quantum processing units. We assess the proposed framework using nontrivial benchmark problems such as damped harmonic oscillators and Nonlinear Autoregressive Moving Average sequences. Our results demonstrate that the distributed QLSTM achieves stable convergence and improved training dynamics compared to classical approaches. This work underscores the potential of modular, distributed quantum computing architectures for large-scale sequence modelling, providing a foundation for the future integration of hybrid quantum-classical solutions into advanced Quantum High-performance computing (HPC) ecosystems.
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。