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arXiv 2303.01461quant-phcond-mat.dis-nn
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Ising自旋模型经典数据表示中的突现序

Emergent Order in Classical Data Representations on Ising Spin Models

Jorja J. Kirk, Matthew D. Jackson, Daniel J. M. King, Philip Intallura, Mekena Metcalf

摘要:

将经典数据编码在量子自旋哈密顿量上产生有序的自旋基态,其用于区分用于二进制分类的数据类型。伊辛哈密顿量是一种典型的自旋模型,用于将经典数据编码到量子位上,称为ZZ特征图。我们评估的基态的伊辛哈密顿编码与三个单独的数据集,包含两类数据。提出了一种利用编码伊辛哈密顿量的基态来预测特定数据类的新方法。在量子计算机上通过量子模拟得到基态观测值,并利用期望值构造了状态空间上的经典概率分布。我们的方法是一个低维表示的指数大的特征空间。反铁磁基态是一维链状晶格和二维正方形晶格的稳定基态。挫折感在用数据编码的三角晶格上诱导出独特的有序状态,暗示了模型潜在相图的可能性。我们研究了数据缩放和数据噪声的阶稳定性。

英文摘要:

Encoding classical data on quantum spin Hamiltonians yields ordered spin ground states which are used to discriminate data types for binary classification. The Ising Hamiltonian is a typical spin model to encode classical data onto qubits, known as the ZZ feature map. We assess the ground states of the Ising Hamiltonian encoded with three separate data sets containing two classes of data. A new methodology is proposed to predict a certain data class using the ground state of the encoded Ising Hamiltonian. Ground state observables are obtained through quantum simulation on a quantum computer, and the expectation values are used to construct a classical probability distribution on the state space. Our approach is a low dimensional representation of the exponentially large feature space. The antiferromagnetic ground state is the stable ground state for the one dimensional chain lattice and the 2D square lattice. Frustration induces unique ordered states on the triangle lattice encoded with data, hinting at the possibility for an underlying phase diagram for the model. We examine order stability with data scaling and data noise.

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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