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arXiv 1807.03325cond-mat.str-elcond-mat.dis-nnquant-ph
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对称性与神经网络量子态的多体激发态

Symmetries and many-body excited states with neural-network quantum states

Kenny Choo, Giuseppe Carleo, Nicolas Regnault, Titus Neupert

摘要:

近年来,人工神经网络已被引入作为一种通用的模型来复杂地表示多体波函数.结合变分蒙特卡罗方法,该方法已被应用于求解哈密顿基态及其能量.在这里,我们提供了这种方法的扩展,研究激发态的性质,在几个多体量子计算的中心任务。首先,我们给出了一个处方,允许目标本征态的(非局部)对称的哈密顿量。其次,我们给出了一个算法,允许计算低的激发态没有对称性。我们证明了我们的方法与限制玻尔兹曼机状态和前馈神经网络作为变分波函数。结果显示一维自旋1/2海森堡模型,和一维玻色-哈伯德模型。当比较可用的精确结果,我们得到了很好的协议的激发态能量的大范围内。有趣的是,我们还发现,对于高能状态,深度网络通常优于浅层架构。

英文摘要:

Artificial neural networks have been recently introduced as a general ansatz to compactly represent many- body wave functions. In conjunction with Variational Monte Carlo, this ansatz has been applied to find Hamil- tonian ground states and their energies. Here we provide extensions of this method to study properties of ex- cited states, a central task in several many-body quantum calculations. First, we give a prescription that allows to target eigenstates of a (nonlocal) symmetry of the Hamiltonian. Second, we give an algorithm that allows to compute low-lying excited states without symmetries. We demonstrate our approach with both Restricted Boltzmann machines states and feedforward neural networks as variational wave-functions. Results are shown for the one-dimensional spin-1/2 Heisenberg model, and for the one-dimensional Bose-Hubbard model. When comparing to available exact results, we obtain good agreement for a large range of excited-states energies. Interestingly, we also find that deep networks typically outperform shallow architectures for high-energy states.

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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