Battaglia Demian A, Santoro Giuseppe E, Tosatti Erio
International School for Advanced Studies (SISSA), and INFM Democritos National Simulation Center, Via Beirut 2-4, I-34014 Trieste, Italy.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Jun;71(6 Pt 2):066707. doi: 10.1103/PhysRevE.71.066707. Epub 2005 Jun 29.
The path integral Monte Carlo simulated quantum annealing algorithm is applied to the optimization of a large hard instance of the random satisfiability problem (N = 10,000). The dynamical behavior of the quantum and the classical annealing are compared, showing important qualitative differences in the way of exploring the complex energy landscape of the combinatorial optimization problem. At variance with the results obtained for the Ising spin glass and for the traveling salesman problem, in the present case the linear-schedule quantum annealing performance is definitely worse than classical annealing. Nevertheless, a quantum cooling protocol based on field-cycling and able to outperform standard classical simulated annealing over short time scales is introduced.
路径积分蒙特卡罗模拟量子退火算法被应用于优化随机可满足性问题的一个大型难实例（N = 10000）。对量子退火和经典退火的动力学行为进行了比较，结果表明在探索组合优化问题复杂能量景观的方式上存在重要的定性差异。与伊辛自旋玻璃和旅行商问题所得到的结果不同，在当前情况下，线性调度量子退火的性能明显比经典退火差。然而，引入了一种基于场循环且在短时间尺度上能够优于标准经典模拟退火的量子冷却协议。
