Abstract
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.
Abstract (translated)
最近在量子计算和机器学习领域的进展催生了量子机器学习（QML），特别是在处理序列数据方面引起了越来越多的兴趣。例如，像QLSTM这样的量子递归模型对于时间序列预测、自然语言处理以及强化学习都表现出潜力。然而，设计有效的变分量子电路（VQC）仍然具有挑战性，并且往往需要针对具体任务进行定制。为了解决这个问题，我们提出了DiffQAS-QLSTM，这是一个端到端可微框架，它在训练过程中同时优化VQC参数和架构选择。我们的实验结果显示，与人工设计的基准模型相比，DiffQAS-QLSTM能够持续获得更低的损失值，在各种测试设置中表现出色。这种方法为规模化的、适应性强的量子序列学习打开了大门。
URL
https://arxiv.org/abs/2508.14955
