基于受限玻尔兹曼机的神经网络量子态横场化模型的纠缠熵计算

IF 2.9 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

The European Physical Journal Plus Pub Date : 2025-10-03 DOI:10.1140/epjp/s13360-025-06855-3

Zhekai Yin, Zixuan Li, Fei Fang Chung, Kok Wee Song, C. K. Ong

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引用次数: 0

摘要

在受限玻尔兹曼机（RBM）的基础上，研究了横场Ising模型的神经网络量子态波函数。训练RBM的输入数据是使用Metropolis-Hastings算法生成的。通过最小化哈密顿量的期望值来优化RBM参数以产生量子基态。我们随后使用这个基态计算二部纠缠熵（EE）。结果表明，在临界点处，EE与自旋链的大小成正比，在大外场存在下，EE呈面积律缩放。

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Calculation of entanglement entropy of transverse-field ising model from neural network quantum state based on a restricted Boltzmann machine

We study transverse-field Ising model by representing its neural network quantum state wavefunction based on a restricted Boltzmann machine (RBM). The input data for training the RBM is generated using the Metropolis–Hastings algorithm. The RBM parameters are optimized by minimizing the expectation value of the Hamiltonian to generate the quantum ground state. We subsequently use this ground state to compute bipartite entanglement entropy (EE). The results show that EE is proportional to the size of the spin chain at the critical point, and exhibits an area law scaling in the presence of a large external field.

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来源期刊

The European Physical Journal Plus PHYSICS, MULTIDISCIPLINARY-

CiteScore

5.40

自引率

8.80%

发文量

1150

审稿时长

4-8 weeks

期刊介绍： The aims of this peer-reviewed online journal are to distribute and archive all relevant material required to document, assess, validate and reconstruct in detail the body of knowledge in the physical and related sciences. The scope of EPJ Plus encompasses a broad landscape of fields and disciplines in the physical and related sciences - such as covered by the topical EPJ journals and with the explicit addition of geophysics, astrophysics, general relativity and cosmology, mathematical and quantum physics, classical and fluid mechanics, accelerator and medical physics, as well as physics techniques applied to any other topics, including energy, environment and cultural heritage.