论文标题

改进的量子哈密顿量子的产品状态近似算法

Improved Product-state Approximation Algorithms for Quantum Local Hamiltonians

论文作者

Bergamaschi, Thiago

论文摘要

基态能量和量子量子的自由能是量子多体物理学的基本数量,但是,总体上估算它们是QMA的含量。在本文中,我们开发了新技术,以在某些量子$ k $ - 本地哈密顿量的某些家族中找到这些数量的经典,添加性错误产品状态。也就是说,那些要么密集,具有较低的阈值等级,要么在稀疏图上定义,该图形不包括固定的未成年人,基于Brandão和Harrow,Gharibian和Kempe所研究的方法和系统,以及Bansal,Bravyi和Bravyi和Terhal。 我们提出两个主要的技术贡献。首先,我们讨论当地哈密顿人的产品状态近似值与组合图属性测试之间的联系。我们为$ k $ loc的哈密顿人开发了一系列薄弱的Szemerédi规律性引理,建立在Frieze和Kannan等。我们使用它们来开发恒定的时间抽样算法,并表征本地汉密尔顿问题的“顶点样本复杂性”,这是Alon,de la Vega,Kannan和Karpinski的经典结果的类似物。其次,我们基于布兰德奥和哈罗的信息理论产品状态近似技术,将其结果扩展到自由能和不对称的图形设置。我们利用这种结构来定义低温下自由能的算法系列,以及某些稀疏图系列的新算法。

The ground state energy and the free energy of Quantum Local Hamiltonians are fundamental quantities in quantum many-body physics, however, it is QMA-Hard to estimate them in general. In this paper, we develop new techniques to find classical, additive error product-state approximations for these quantities on certain families of Quantum $k$-Local Hamiltonians. Namely, those which are either dense, have low threshold rank, or are defined on a sparse graph that excludes a fixed minor, building on the methods and the systems studied by Brandão and Harrow, Gharibian and Kempe, and Bansal, Bravyi and Terhal. We present two main technical contributions. First, we discuss a connection between product-state approximations of local Hamiltonians and combinatorial graph property testing. We develop a series of weak Szemerédi regularity lemmas for $k$-local Hamiltonians, built on those of Frieze and Kannan and others. We use them to develop constant time sampling algorithms, and to characterize the `vertex sample complexity' of the Local Hamiltonian problem, in an analog to a classical result by Alon, de la Vega, Kannan and Karpinski. Second, we build on the information-theoretic product-state approximation techniques by Brandão and Harrow, extending their results to the free energy and to an asymmetric graph setting. We leverage this structure to define families of algorithms for the free energy at low temperatures, and new algorithms for certain sparse graph families.

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