标题： 量子长短期记忆网络与可微架构搜索 显示英文标题

标题： Quantum Long Short-term Memory with Differentiable Architecture Search

摘要： 近年来，量子计算和机器学习的进展催生了量子机器学习（QML），人们对从序列数据中学习的兴趣日益增长。 像QLSTM这样的量子循环模型在时间序列预测、自然语言处理和强化学习中具有前景。 然而，设计有效的变分量子电路（VQCs）仍然具有挑战性，并且通常依赖于特定任务。 为了解决这个问题，我们提出了DiffQAS-QLSTM，这是一种端到端可微框架，在训练过程中优化VQC参数和架构选择。 我们的结果表明，DiffQAS-QLSTM始终优于手工设计的基线，实现了在多种测试设置下的更低损失。 这种方法为可扩展和自适应的量子序列学习打开了大门。 显示英文摘要

摘要： Recent advances in quantum computing and machine learning have given rise to quantum machine learning (QML), with growing interest in learning from sequential data. Quantum recurrent models like QLSTM are promising for time-series prediction, NLP, and reinforcement learning. However, designing effective variational quantum circuits (VQCs) remains challenging and often task-specific. To address this, we propose DiffQAS-QLSTM, an end-to-end differentiable framework that optimizes both VQC parameters and architecture selection during training. Our results show that DiffQAS-QLSTM consistently outperforms handcrafted baselines, achieving lower loss across diverse test settings. This approach opens the door to scalable and adaptive quantum sequence learning.