Learning a compass spin model with neural network quantum states
Learning a compass spin model with neural network quantum states
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DOI：
10.1088/1361-648x/ac43ff
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Eric Zou;Erik. Long;E. Zhao
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文献类型：
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作者：
Eric Zou;Erik. Long;E. Zhao
神经网络量子态为相互作用量子系统的多体态提供了一种新的表示，并为解决挫败的量子自旋模型开辟了一条有前途的路线，这些模型逃避了其他数值方法。然而，它描述具有大晶胞的复杂磁序的能力尚未得到证明，并且它在崎岖的能源景观中的性能受到质疑。本文利用限制玻尔兹曼机（RBM）和随机梯度下降法，在蜂窝晶格上寻找一个罗盘自旋模型的基态，该模型统一了Kitaev模型、Ising模型和量子120°模型.我们报告的变分能量，序参数和相关函数的计算结果。得到的相图与张量网络假设的预测非常一致，证明了RBM学习受抑量子自旋Hamilton基态的能力。讨论了计算的局限性。本文概述了一些策略，以解决机器学习中遇到的一些挑战。
Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines (RBMs) and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120° model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of RBMs in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets.