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arXiv 1502.05385cond-mat.str-el
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张量网络重正化得到了多尺度纠缠重正化解

Tensor network renormalization yields the multi-scale entanglement renormalization ansatz

Glen Evenbly, Guifre Vidal

摘要:

我们利用最近提出的$H$(TNR)[G. Evenbly和G. Vidal,arXiv:1412.0732]转换为用于无限$β$的欧几里得时间演化算子$e^{-βH}$。这种方法绕过了以前的MERA算法的昂贵的能量最小化,当应用于有限逆温度$β$,产生一个热吉布斯状态的MERA表示。我们的构造赋予TNR波函数和哈密顿量空间中的重整化群流(而不仅仅是在更抽象的张量空间中),并将MERA形式主义扩展到经典统计系统。

英文摘要:

We show how to build a multi-scale entanglement renormalization ansatz (MERA) representation of the ground state of a many-body Hamiltonian $H$ by applying the recently proposed \textit{tensor network renormalization} (TNR) [G. Evenbly and G. Vidal, arXiv:1412.0732] to the Euclidean time evolution operator $e^{-βH}$ for infinite $β$. This approach bypasses the costly energy minimization of previous MERA algorithms and, when applied to finite inverse temperature $β$, produces a MERA representation of a thermal Gibbs state. Our construction endows TNR with a renormalization group flow in the space of wave-functions and Hamiltonians (and not just in the more abstract space of tensors) and extends the MERA formalism to classical statistical systems.

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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