量子多体团簇中组态相互作用问题的神经网络支持基组优化器：可行性研究与数值证明
Neural-network-supported basis optimizer for the configuration interaction problem in quantum many-body clusters: Feasibility study and numerical proof
AI总结：
本文提出神经网络分类器结合主动学习的方法，在量子多体团簇组态相互作用计算中高效选择斯莱特行列式，以安德森模型验证其在保持精度的同时显著提升基组紧凑性与计算效率。
摘要：
本文提出了一种深度学习方法，用于优化凝聚态量子多体系统组态相互作用计算中斯莱特行列式的选择。我们在最多包含299个浴位点的单杂质安德森模型的离散版本上示例了该算法。通过采用神经网络分类器和主动学习，我们的算法通过迭代识别对基态波函数最相关的斯莱特行列式来提高计算效率。我们将结果与已有方法进行基准对比，并研究了该方法与其他基组截断方案相比的效率。我们的算法在行列式选择效率上展现出显著提升，在不牺牲精度的前提下得到了更紧凑且计算上更易处理的基组。鉴于我们的神经网络支持的选择方案可直接应用于量子多体团簇的其他模型哈密顿量，该算法能够显著推进关联凝聚态背景下选择性组态相互作用计算的发展。
英文摘要：
A deep-learning approach to optimize the selection of Slater determinants in configuration interaction calculations for condensed-matter quantum many-body systems is developed. We exemplify our algorithm on the discrete version of the single-impurity Anderson model with up to 299 bath sites. Employing a neural network classifier and active learning, our algorithm enhances computational efficiency by iteratively identifying the most relevant Slater determinants for the ground-state wavefunction. We benchmark our results against established methods and investigate the efficiency of our approach as compared to other basis truncation schemes. Our algorithm demonstrates a substantial improvement in the efficiency of determinant selection, yielding a more compact and computationally manageable basis without compromising accuracy. Given the straightforward application of our neural network-supported selection scheme to other model Hamiltonians of quantum many-body clusters, our algorithm can significantly advance selective configuration interaction calculations in the context of correlated condensed matter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
