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arXiv 1006.4147quant-phcond-mat.dis-nncond-mat.stat-mechcs.DS
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绝热量子优化处理器的性能研究

Investigating the Performance of an Adiabatic Quantum Optimization Processor

Kamran Karimi, Neil G. Dickson, Firas Hamze, M. H. S. Amin, Marshall Drew-Brook, Fabian A. Chudak, Paul I. Bunyk, William G. Macready, Geordie Rose

摘要:

绝热量子优化为求解困难优化问题提供了一种新的方法。在本文中,我们计算的绝热量子优化NP难伊辛自旋玻璃实例类与多达128个二进制变量的过程中的最小间隙确定的中值绝热时间(秒)。使用从一个现实的超导绝热量子处理器获得的参数,我们提取的最小间隙和矩阵元素,使用高性能的量子蒙特卡罗模拟在一个大规模的基于互联网的计算平台。我们比较了两个经典的求解器的中值运行时间与中值绝热时间,发现对于所考虑的问题大小,模拟处理器架构的绝热时间比两个经典求解器的时间短约4和6个数量级。这表明,如果绝热时间尺度决定计算时间,绝热量子优化将明显优于那些经典的求解器的中值自旋玻璃问题的至少高达128量子位。我们还讨论了重要的附加约束,影响一个现实的系统的性能。

英文摘要:

Adiabatic quantum optimization offers a new method for solving hard optimization problems. In this paper we calculate median adiabatic times (in seconds) determined by the minimum gap during the adiabatic quantum optimization for an NP-hard Ising spin glass instance class with up to 128 binary variables. Using parameters obtained from a realistic superconducting adiabatic quantum processor, we extract the minimum gap and matrix elements using high performance Quantum Monte Carlo simulations on a large-scale Internet-based computing platform. We compare the median adiabatic times with the median running times of two classical solvers and find that, for the considered problem sizes, the adiabatic times for the simulated processor architecture are about 4 and 6 orders of magnitude shorter than the two classical solvers' times. This shows that if the adiabatic time scale were to determine the computation time, adiabatic quantum optimization would be significantly superior to those classical solvers for median spin glass problems of at least up to 128 qubits. We also discuss important additional constraints that affect the performance of a realistic system.

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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