用课程学习加速量子多体系统计算，提升效率与稳定性。
Explorative Curriculum Learning for Strongly Correlated Electron Systems
- 基于迁移学习设计课程学习框架，分阶段优化参数空间。
- 实现约200倍计算提速，优化过程更稳定。
- 适合研究强关联电子系统的科研人员参考。
神经网络量子态（NQS）在复杂量子多体系统如强关联电子系统中实现了高精度预测，但计算成本仍过高，导致对相互作用强度等物理参数的探索效率低下。尽管迁移学习被提出以缓解此问题，但在大规模系统和多样化参数范围下的泛化能力仍受限。为此，我们提出一种基于迁移学习的新型课程学习框架，可高效稳定地探索量子多体系统的广阔参数空间。通过摄动视角解析NQS迁移学习，证明了先验物理知识可灵活融入课程学习过程。我们还提出Pairing-Net架构，用于实践该策略，并实证其有效性。结果表明，相比传统方法，计算速度提升约200倍，优化稳定性显著增强。
原文摘要 · Abstract (English)
Recent advances in neural network quantum states (NQS) have enabled high-accuracy predictions for complex quantum many-body systems such as strongly correlated electron systems. However, the computational cost remains prohibitive, making exploration of the diverse parameters of interaction strengths and other physical parameters inefficient. While transfer learning has been proposed to mitigate this challenge, achieving generalization to large-scale systems and diverse parameter regimes remains difficult. To address this limitation, we propose a novel curriculum learning framework based on transfer learning for NQS. This facilitates efficient and stable exploration across a vast parameter space of quantum many-body systems. In addition, by interpreting NQS transfer learning through a perturbative lens, we demonstrate how prior physical knowledge can be flexibly incorporated into the curriculum learning process. We also propose Pairing-Net, an architecture to practically implement this strategy for strongly correlated electron systems, and empirically verify its effectiveness. Our results show an approximately 200-fold speedup in computation and a marked improvement in optimization stability compared to conventional methods.
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