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arXiv 2012.03019quant-phcond-mat.str-elcs.LG
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用于多体哈密顿量估计的深度学习局部约化密度矩阵

Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation

Beijing Normal University(北京师范大学);Capital Normal University(首都师范大学)

Xinran Ma, Z. C. Tu, Shi-Ju Ran

摘要:

人类专家不能通过简单地“读取”系数来有效地获取量子多体态的物理信息,而必须根据序参量和量子测量等先验知识进行回答。在这项工作中,我们证明了卷积神经网络(CNN)可以从局部约化密度矩阵的系数学习,以估计多体哈密顿量的物理参数,如耦合强度和磁场,提供的状态为基态。我们提出了QubismNet,它由两个主要部分组成:将基态(或纯化的约化密度矩阵)可视化为图像的Qubism映射,以及将图像映射到目标物理参数的CNN。为了平衡起见,QubismNet对训练集假设了某些约束,在几个量子自旋模型上表现出令人印象深刻的学习和泛化能力。虽然训练样本仅限于某些参数范围内的状态,但QubismNet可以准确地估计超出这些训练区域的状态参数。例如,我们的结果表明,QubismNet可以通过从远离临界附近的状态学习来估计临界点附近的磁场。我们的工作阐明了一种数据驱动的方式来推断给出设计基态的哈密顿量,因此将有利于现有和未来的量子技术的推广,如基于哈密顿量的量子模拟和状态层析成像。

英文摘要:

Human experts cannot efficiently access the physical information of quantum many-body states by simply "reading" the coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements. In this work, we demonstrate that convolutional neural network (CNN) can learn from the coefficients of local reduced density matrices to estimate the physical parameters of the many-body Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states. We propose QubismNet that consists of two main parts: the Qubism map that visualizes the ground states (or the purified reduced density matrices) as images, and a CNN that maps the images to the target physical parameters. By assuming certain constraints on the training set for the sake of balance, QubismNet exhibits impressive powers of learning and generalization on several quantum spin models. While the training samples are restricted to the states from certain ranges of the parameters, QubismNet can accurately estimate the parameters of the states beyond such training regions. For instance, our results show that QubismNet can estimate the magnetic fields near the critical point by learning from the states away from the critical vicinity. Our work illuminates a data-driven way to infer the Hamiltonians that give the designed ground states, and therefore would benefit the existing and future generalizations of quantum technologies such as Hamiltonian-based quantum simulations and state tomography.

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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