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arXiv 2604.04435cond-mat.quant-gasnucl-thphysics.chem-phphysics.comp-ph
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神经网络量子态用于解决少数体问题:应用于Efimov物理

Neural-network quantum states for solving few-body problems: application to Efimov physics

University of Electro-Communications(电波大学)

Sora Yokoi, Shimpei Endo, Hiroki Saito

AI总结:

本文利用神经网络量子态方法研究连续空间中强相互作用少数体问题,计算Efimov态及少数体束缚态,验证了该方法在三到六体系统中的有效性。

摘要:

神经网络量子态最近已发展为解决量子多体问题的强大方法,在晶格系统中取得了显著成功。本文将该方法扩展到连续空间中强相互作用的少数体问题,并通过计算Efimov态及相关少数体束缚态来展示其能力。使用全连接前馈神经网络与雅可比坐标作为输入,结合投影方法,我们计算了单位arity下三到六体相同玻色子系统的基态和第一激发态,以及由两个相同费米子和第三个粒子组成的质量不平衡费米子系统。所获得的基态和第一激发态能量与之前报道的结果一致。此外,所提出的方法还再现了Efimov态的关键特征,包括离散标度不变性、波函数的特征几何结构以及质量不平衡费米子系统中的临界质量行为。我们的方法可以轻松应用于连续空间中广泛类别的强相关少数体问题。

英文摘要:

Neural-network quantum states have recently emerged as a powerful method for solving quantum many-body problems, with notable successes in lattice systems. Here, we extend this approach to strongly interacting few-body problems in continuous space, and demonstrate its capability by computing the Efimov states and associated few-body bound states. Using a fully connected feedforward neural network with Jacobi coordinates as inputs, combined with a projection method, we compute the ground and first excited states for three- to six-body systems of identical bosons at unitarity, as well as a mass-imbalanced fermionic system consisting of two identical fermions and a third particle. The obtained energies of the ground and first excited states agree well with previously reported results. Furthermore, the proposed approach also reproduces key features of Efimov states, including the discrete scale invariance, the characteristic geometric structure of the wave function, and the critical-mass behavior in mass-imbalanced fermionic systems. Our method can be readily applied to a broad class of strongly correlated few-body problems in continuous space.

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。

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