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arXiv 1102.0531quant-phcond-mat.stat-mech
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高维Hilbert空间中能量约束量子叠加的Monte-Carlo抽样

Monte-Carlo sampling of energy-constrained quantum superpositions in high-dimensional Hilbert spaces

Frank Hantschel, Boris V. Fine

摘要:

最近的研究在高维希尔伯特空间中的量子统计系综的性质遇到了困难与Monte-Carlo抽样的量子叠加的能量期望值的限制。一个简单的蒙特卡罗程序将能量约束流形封闭在一个更大的流形内,这是很容易采样的,例如,一个超立方体。这样的采样例程的效率随着希尔伯特空间的维度的增加而指数地降低,因为封闭流形的体积变得指数地大于感兴趣的流形的体积。本文探讨了如何通过改变包围能量约束流形的流形的形状来优化上述例程。对于14维希尔伯特空间,采样效率的改进约为5倍。上述算法的优点是,它不损害严格的统计性质的抽样结果,因此可以用来测试其他更复杂的蒙特卡洛例程。目前的尝试,以优化封闭的流形也带来了洞察的能量约束流形本身的几何性质。

英文摘要:

Recent studies into the properties of quantum statistical ensembles in high-dimensional Hilbert spaces have encountered difficulties associated with the Monte-Carlo sampling of quantum superpositions constrained by the energy expectation value. A straightforward Monte-Carlo routine would enclose the energy constrained manifold within a larger manifold, which is easy to sample, for example, a hypercube. The efficiency of such a sampling routine decreases exponentially with the increase of the dimension of the Hilbert space, because the volume of the enclosing manifold becomes exponentially larger than the volume of the manifold of interest. The present paper explores the ways to optimise the above routine by varying the shapes of the manifolds enclosing the energy-constrained manifold. The resulting improvement in the sampling efficiency is about a factor of five for a 14-dimensional Hilbert space. The advantage of the above algorithm is that it does not compromise on the rigorous statistical nature of the sampling outcome and hence can be used to test other more sophisticated Monte-Carlo routines. The present attempts to optimise the enclosing manifolds also bring insights into the geometrical properties of the energy constrained manifold itself.

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