中文标题#
量子長短期記憶網絡與可微架構搜索
英文标题#
Quantum Long Short-term Memory with Differentiable Architecture Search
中文摘要#
近年來，量子計算和機器學習的進展催生了量子機器學習（QML），人們對從序列數據中學習的興趣日益增長。 像 QLSTM 這樣的量子循環模型在時間序列預測、自然語言處理和強化學習中具有前景。 然而，設計有效的變分量子電路（VQCs）仍然具有挑戰性，並且通常依賴於特定任務。 為了解決這個問題，我們提出了 DiffQAS-QLSTM，這是一種端到端可微框架，在訓練過程中優化 VQC 參數和架構選擇。 我們的結果表明，DiffQAS-QLSTM 始終優於手工設計的基線，實現了在多種測試設置下的更低損失。 這種方法為可擴展和自適應的量子序列學習打開了大門。
英文摘要#
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.
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