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arXiv 1004.1335cond-mat.stat-mechhep-lathep-thquant-ph
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蒙特卡罗抽样的统计力学与符号问题

Statistical mechanics of Monte Carlo sampling and the sign problem

Gustavo During, Jorge Kurchan

摘要:

任何系统的蒙特卡罗采样都可以用相关的玻璃模型(随机能量模型的一种变体)进行分析,只要有符号问题,就有复杂的场。该模型有三种类型的阶段(液体,冻结和“混沌”),这是具有复杂参数的玻璃模型的特点。只有液体的问题才能得到原问题的正确答案,而我们的任务是设计模拟,使其停留在它的内部。在这些条件下,可以研究采样到正确期望值的统计收敛性,得到作为真实系统和参考系统之间自由能差的函数的计算机时间的一般下限。以这种方式,可以优化重要性采样策略。

英文摘要:

Monte Carlo sampling of any system may be analyzed in terms of an associated glass model -- a variant of the Random Energy Model -- with, whenever there is a sign problem, complex fields. This model has three types of phases (liquid, frozen and `chaotic'), as is characteristic of glass models with complex parameters. Only the liquid one yields the correct answers for the original problem, and the task is to design the simulation to stay inside it. The statistical convergence of the sampling to the correct expectation values may be studied in these terms, yielding a general lower bound for the computer time as a function of the free energy difference between the true system, and a reference one. In this way, importance-sampling strategies may be optimized.

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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