论文标题
Ising机器作为组合优化问题的硬件求解器
Ising machines as hardware solvers of combinatorial optimization problems
论文作者
论文摘要
Ising机器是硬件求解器，旨在找到Ising模型的绝对或近似接地状态。 ISING模型具有基本的计算兴趣，因为可以将复杂性NP类中的任何问题作为仅多项式开销的ISIN问题。一款胜过现有标准数字计算机的可扩展式机器可能会对各种优化问题产生巨大影响。在这篇综述中，我们调查了构建伊辛机器的各种方法的当前状态，并解释其基本的运营原则。此处考虑的ISING机器的类型包括基于SpinTronics，Optics，Memristors和Digital Hartware Accelerators等技术的经典热灭火器；用光学和电子设备实现的动力系统求解器；和超导电路量子退火器。我们使用标准指标进行比较和对比他们的性能，例如地面成功概率和解决时间，使其与问题大小的扩展关系，并讨论其优势和劣势。
Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications for a wide variety of optimization problems. In this review, we survey the current status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as spintronics, optics, memristors, and digital hardware accelerators; dynamical-systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and discuss their strengths and weaknesses.
