提出量子架构搜索新方法，解决量子电路训练中的遗忘与编码难题。
Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing
- 用张量列车编码压缩高维信号，降低量子资源消耗
- 双循环优化分离参数与结构搜索，避免灾难性遗忘
- 在心电图与金融数据上表现优异，适合近中期量子设备
我们提出CL-QAS，一种持续量子架构搜索框架，缓解变分量子电路中高昂的振幅编码和灾难性遗忘问题。该方法采用张量列车编码，将高维随机信号高效压缩为低秩量子特征表示。通过双环学习策略，将电路参数优化与架构探索分离，并引入弹性权重巩固正则化以保证多任务间的稳定性。我们推导了在量子噪声下逼近误差、泛化能力和鲁棒性的理论上限，证明CL-QAS具备可控表达能力、样本高效的泛化性能以及无荒漠平原的平滑收敛。在基于心电图的信号分类和金融时间序列预测上的实证评估表明，其在准确率、平衡准确率、F1分数和奖励值上均有显著提升。CL-QAS保持强前向与后向迁移能力，在去极化和读出噪声下表现出有界退化，展现了其在近中期量子设备上实现自适应、抗噪学习的巨大潜力。
原文摘要 · Abstract (English)
We introduce CL-QAS, a continual quantum architecture search framework that mitigates the challenges of costly amplitude encoding and catastrophic forgetting in variational quantum circuits. The method uses Tensor-Train encoding to efficiently compress high-dimensional stochastic signals into low-rank quantum feature representations. A bi-loop learning strategy separates circuit parameter optimization from architecture exploration, while an Elastic Weight Consolidation regularization ensures stability across sequential tasks. We derive theoretical upper bounds on approximation, generalization, and robustness under quantum noise, demonstrating that CL-QAS achieves controllable expressivity, sample-efficient generalization, and smooth convergence without barren plateaus. Empirical evaluations on electrocardiogram (ECG)-based signal classification and financial time-series forecasting confirm substantial improvements in accuracy, balanced accuracy, F1 score, and reward. CL-QAS maintains strong forward and backward transfer and exhibits bounded degradation under depolarizing and readout noise, highlighting its potential for adaptive, noise-resilient quantum learning on near-term devices.
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