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arXiv 1908.03740cond-mat.stat-mechquant-ph
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排列矩阵表示量子蒙特卡罗

Permutation Matrix Representation Quantum Monte Carlo

Lalit Gupta, Tameem Albash, Itay Hen

摘要:

我们提出了一个量子蒙特卡罗算法的模拟一般的量子和经典的多体模型在一个统一的框架。该算法建立在量子配分函数的非对角项的幂级数展开上,并且是无参数和无Trotter误差的。在我们的方法中,量子维度由置换群的元素的乘积组成。因此,它允许在平等的基础上研究各种各样的模型。为了证明我们的技术的实用性,我们用它来澄清出现的符号问题的非随机物理模型的模拟。我们展示了我们的算法的灵活性和现有的国家的最先进的通过模拟横向场伊辛模型哈密顿和比较我们的技术对随机级数展开算法的性能,它提供的优势。我们还研究了随机选择的两体横场相互作用增强的横场伊辛模型。

英文摘要:

We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We showcase the flexibility of our algorithm and the advantages it offers over existing state-of-the-art by simulating transverse-field Ising model Hamiltonians and comparing the performance of our technique against that of the stochastic series expansion algorithm. We also study a transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions.

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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