自动优化量子循环网络架构，提升时序数据处理能力
Quantum Long Short-term Memory with Differentiable Architecture Search
- 用可微分搜索自动设计量子电路结构
- 在多种测试中损失更低，表现优于人工设计模型
- 适合量子机器学习、时序预测等方向研究者
量子计算与机器学习的融合催生了量子机器学习（QML），对序列数据的学习兴趣日益增长。量子循环模型如QLSTM在时间序列预测、自然语言处理和强化学习中展现潜力，但有效变分量子电路（VQC）的设计仍具挑战性且高度依赖任务。为此，我们提出DiffQAS-QLSTM，一种端到端可微分框架，在训练过程中同时优化VQC参数与架构选择。结果表明，该方法在多种测试设置下均显著优于人工设计基线，实现更低损失。这一方法为可扩展、自适应的量子序列学习开辟了新路径。
原文摘要 · Abstract (English)
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.
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