D-Wave Systems Inc., 100-4401 Still Creek Drive, Burnaby, British Columbia V5C 6G9, Canada.
Nature. 2011 May 12;473(7346):194-8. doi: 10.1038/nature10012.
Many interesting but practically intractable problems can be reduced to that of finding the ground state of a system of interacting spins; however, finding such a ground state remains computationally difficult. It is believed that the ground state of some naturally occurring spin systems can be effectively attained through a process called quantum annealing. If it could be harnessed, quantum annealing might improve on known methods for solving certain types of problem. However, physical investigation of quantum annealing has been largely confined to microscopic spins in condensed-matter systems. Here we use quantum annealing to find the ground state of an artificial Ising spin system comprising an array of eight superconducting flux quantum bits with programmable spin-spin couplings. We observe a clear signature of quantum annealing, distinguishable from classical thermal annealing through the temperature dependence of the time at which the system dynamics freezes. Our implementation can be configured in situ to realize a wide variety of different spin networks, each of which can be monitored as it moves towards a low-energy configuration. This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples. Moreover, with an increased number of spins, such a system may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.
许多有趣但实际上难以解决的问题都可以归结为寻找相互作用的自旋系统的基态;然而,找到这样的基态在计算上仍然很困难。人们相信,一些自然发生的自旋系统的基态可以通过一种称为量子退火的过程有效地达到。如果能够利用量子退火,它可能会改进已知的解决某些类型问题的方法。然而,对量子退火的物理研究在很大程度上局限于凝聚态系统中的微观自旋。在这里,我们使用量子退火来找到由八个超导通量量子位组成的人工伊辛自旋系统的基态,这些量子位具有可编程的自旋-自旋耦合。我们观察到了量子退火的明显特征,通过系统动力学冻结时的时间与温度的依赖关系,可以与经典热退火区分开来。我们的实现可以现场配置,以实现各种不同的自旋网络,每个网络都可以在向低能构象移动时进行监测。这个可编程的人工自旋网络在理想孤立自旋网络的理论研究和大块磁性样品的实验研究之间架起了桥梁。此外,随着自旋数量的增加,这样的系统可能为实现量子算法提供一种实际的物理手段,可能允许更有效地解决某些类的硬组合优化问题。