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Rapid mixing of path integral Monte Carlo for 1D stoquastic Hamiltonians
arXiv - CS - Data Structures and Algorithms Pub Date : 2018-12-05 , DOI: arxiv-1812.02144
Elizabeth Crosson, Aram W. Harrow

Path integral quantum Monte Carlo (PIMC) is a method for estimating thermal equilibrium properties of stoquastic quantum spin systems by sampling from a classical Gibbs distribution using Markov chain Monte Carlo. The PIMC method has been widely used to study the physics of materials and for simulated quantum annealing, but these successful applications are rarely accompanied by formal proofs that the Markov chains underlying PIMC rapidly converge to the desired equilibrium distribution. In this work we analyze the mixing time of PIMC for 1D stoquastic Hamiltonians, including disordered transverse Ising models (TIM) with long-range algebraically decaying interactions as well as disordered XY spin chains with nearest-neighbor interactions. By bounding the convergence time to the equilibrium distribution we rigorously justify the use of PIMC to approximate partition functions and expectations of observables for these models at inverse temperatures that scale at most logarithmically with the number of qubits. The mixing time analysis is based on the canonical paths method applied to the single-site Metropolis Markov chain for the Gibbs distribution of 2D classical spin models with couplings related to the interactions in the quantum Hamiltonian. Since the system has strongly nonisotropic couplings that grow with system size, it does not fall into the known cases where 2D classical spin models are known to mix rapidly.

中文翻译:

一维随机哈密顿量的路径积分蒙特卡罗的快速混合

路径积分量子蒙特卡罗 (PIMC) 是一种通过使用马尔可夫链蒙特卡罗从经典吉布斯分布中采样来估计定常量子自旋系统的热平衡特性的方法。PIMC 方法已被广泛用于研究材料物理和模拟量子退火,但这些成功的应用很少伴随有正式的证据证明 PIMC 下的马尔可夫链迅速收敛到所需的平衡分布。在这项工作中,我们分析了一维随机哈密顿量的 PIMC 混合时间,包括具有长程代数衰减相互作用的无序横向 Ising 模型 (TIM) 以及具有最近邻相互作用的无序 XY 自旋链。通过将收敛时间限制在平衡分布上,我们严格证明使用 PIMC 来逼近这些模型在逆温度下的分区函数和可观测值的期望,这些模型在逆温度下最多与量子比特数成对数关系。混合时间分析基于应用于单点 Metropolis 马尔可夫链的规范路径方法,用于二维经典自旋模型的 Gibbs 分布,其耦合与量子哈密顿量中的相互作用有关。由于系统具有随系统规模增长而增长的强非各向同性耦合,因此它不属于已知二维经典自旋模型快速混合的已知情况。混合时间分析基于应用于单点 Metropolis 马尔可夫链的规范路径方法,用于二维经典自旋模型的 Gibbs 分布,其耦合与量子哈密顿量中的相互作用有关。由于系统具有随系统规模增长而增长的强非各向同性耦合,因此它不属于已知二维经典自旋模型快速混合的已知情况。混合时间分析基于应用于单点 Metropolis 马尔可夫链的规范路径方法,用于二维经典自旋模型的 Gibbs 分布,其耦合与量子哈密顿量中的相互作用有关。由于系统具有随系统规模增长而增长的强非各向同性耦合,因此它不属于已知二维经典自旋模型快速混合的已知情况。
更新日期:2020-03-19
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