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具有横向场驱动哈密顿量的量子退火机器的指数偏差基态采样

Exponentially Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians.

作者信息

Mandrà Salvatore, Zhu Zheng, Katzgraber Helmut G

机构信息

Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.

NASA Ames Research Center Quantum Artificial Intelligence Laboratory (QuAIL), Mail Stop 269-1, Moffett Field, California 94035, USA.

出版信息

Phys Rev Lett. 2017 Feb 17;118(7):070502. doi: 10.1103/PhysRevLett.118.070502.

Abstract

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated with a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)NJOPFM1367-263010.1088/1367-2630/11/7/073021]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

摘要

我们研究了D-Wave 2X量子退火机器在具有良好控制的基态简并性的系统上的性能。虽然获得自旋玻璃基准实例的基态是一项艰巨的任务,但任何优化算法或机器的黄金标准是或多或少以相等的概率对所有使哈密顿量最小化的解进行采样。我们的结果表明,虽然在D-Wave 2X设备上进行朴素的横向场量子退火可以找到问题的基态能量,但它不太适合识别与特定实例相关的所有简并基态配置。更糟糕的是,一些状态被指数抑制,这与之前关于玩具模型问题的研究一致[《新物理学杂志》11, 073021 (2009)NJOPFM1367-263010.1088/1367-2630/11/7/073021]。这些结果表明,未来的量子退火机器需要更复杂的驱动哈密顿量,以确保对基态流形进行公平采样。

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