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arXiv 2104.10102cond-mat.softcond-mat.stat-mechquant-ph
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量子计算的Polymer物理学

Polymer Physics by Quantum Computing

Cristian Micheletti, Philipp Hauke, Pietro Faccioli

摘要:

在计算物理中,即使是在基于格子的模型中,对稠密聚合物混合物的平衡系综进行采样也是一个典型的难题。在这里,我们开发了一种基于相互作用的二进制张量的形式主义,允许使用量子退火机来解决这个问题。我们的方法是一般的,如自回避,分支和循环的属性都可以指定在张量的二次相互作用。然后通过求解合适的离散能量最小化问题无缝地生成不同晶格聚合物系综的微观态实现。这种方法使我们能够利用量子退火机的优势,正如我们使用D-Wave量子计算机从低密度到高密度对聚合物混合物进行采样所展示的那样。我们的系统方法提供了一个很有前途的途径,利用量子计算机的快速发展采样离散模型的丝状软物质系统。

英文摘要:

Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum computer. Our systematic approach offers a promising avenue to harness the rapid development of quantum computers for sampling discrete models of filamentous soft-matter 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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