Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning. However, standard deep learning suffers from a fundamental issue: loss of plasticity, in which networks gradually lose their ability to learn from new data. Here we show that quantum learning models naturally overcome this limitation, preserving plasticity over long timescales. We demonstrate this advantage systematically across a broad spectrum of tasks from multiple learning paradigms, including supervised learning and reinforcement learning, and diverse data modalities, from classical high-dimensional images to quantum-native datasets. Although classical models exhibit performance degradation correlated with unbounded weight and gradient growth, quantum neural networks maintain consistent learning capabilities regardless of the data or task. We identify the origin of the advantage as the intrinsic physical constraints of quantum models. Unlike classical networks where unbounded weight growth leads to landscape ruggedness or saturation, the unitary constraints confine the optimization to a compact manifold. Our results suggest that the utility of quantum computing in machine learning extends beyond potential speedups, offering a robust pathway for building adaptive artificial intelligence and lifelong learners.

翻译：在动态、真实世界环境中，人工智能需要具备持续学习的能力。然而，标准深度学习存在一个根本性问题：可塑性丧失，即网络逐渐失去从新数据中学习的能力。在此，我们证明量子学习模型天然克服了这一限制，能在长时间尺度上保持可塑性。我们系统性地在多个学习范式（包括监督学习和强化学习）的广泛任务谱系中，以及从经典高维图像到量子原生数据集等多样化数据模态下，展示了这一优势。尽管经典模型会表现出与权重和梯度无界增长相关的性能退化，但量子神经网络无论面对何种数据或任务，都能维持一致的学习能力。我们将这一优势的根源归结为量子模型内在的物理约束。与经典网络中无界权重增长导致损失景观崎岖或饱和不同，量子模型中的酉约束将优化限制在一个紧致流形上。我们的结果表明，量子计算在机器学习中的效用超越潜在的速度提升，为构建自适应人工智能和终身学习系统提供了一条稳健的路径。