近日，德国奥格斯堡大学的Ao Chen与Markus Heyl合作并取得一项新进展。经过不懈努力，他们通过高效优化增强深度神经量子态。相关研究成果已于2024年7月1日在国际知名学术期刊《自然—物理学》上发表。
据悉，计算相互作用量子物质的基态是一个长期存在的挑战，特别是对于复杂的二维系统。最近的发展突出了神经量子态通过将多体波函数编码到人工神经网络中，来解决量子多体问题的潜力。然而，该方法面临着现有优化算法不适合训练现代大规模深度网络架构的关键限制。
因此，该研究团队引入了一种最小步长随机重构优化算法，该算法允许研究人员训练具有多达106个参数的深度神经量子态。研究人员展示了他们在正方形和三角形网格上的范例受阻挫自旋-1/2模型的方法，与现有结果相比，他们训练的深度网络接近机器精度，并产生改进的变分能量。利用该优化算法，研究人员在考虑的模型中找到了无能隙量子自旋液相的数值证据，这是迄今为止的一个开放问题。研究人员提出了一种利用大规模人工神经网络的表达能力，来捕捉量子多体问题中出现的复杂性的方法。
附：英文原文
Title: Empowering deep neural quantum states through efficient optimization
Author: Chen, Ao, Heyl, Markus
Issue&Volume: 2024-07-01
Abstract: Computing the ground state of interacting quantum matter is a long-standing challenge, especially for complex two-dimensional systems. Recent developments have highlighted the potential of neural quantum states to solve the quantum many-body problem by encoding the many-body wavefunction into artificial neural networks. However, this method has faced the critical limitation that existing optimization algorithms are not suitable for training modern large-scale deep network architectures. Here, we introduce a minimum-step stochastic-reconfiguration optimization algorithm, which allows us to train deep neural quantum states with up to 106 parameters. We demonstrate our method for paradigmatic frustrated spin-1/2 models on square and triangular lattices, for which our trained deep networks approach machine precision and yield improved variational energies compared to existing results. Equipped with our optimization algorithm, we find numerical evidence for gapless quantum-spin-liquid phases in the considered models, an open question to date. We present a method that captures the emergent complexity in quantum many-body problems through the expressive power of large-scale artificial neural networks.
DOI: 10.1038/s41567-024-02566-1
Source: https://www.nature.com/articles/s41567-024-02566-1