What Is the Optimal Annealing Schedule in Quantum Annealing

被引:0
|
作者
Galindo, Oscar [1 ]
Kreinovich, Vladik [1 ]
机构
[1] Univ Texas El Paso, Dept Comp Sci, 500 W Univ, El Paso, TX 79968 USA
来源
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2020年
基金
美国国家科学基金会;
关键词
Quantum Simulated Annealing; annealing schedules; shift-invariance; scale-invariance;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In many real-life situations in engineering (and in other disciplines), we need to solve an optimization problem: we want an optimal design, we want an optimal control, etc. One of the main problems in optimization is avoiding local maxima (or minima). One of the techniques that helps with solving this problem is annealing: whenever we find ourselves in a possibly local maximum, we jump out with sonic probability and continue search for the true optimum. A natural way to organize such a probabilistic perturbation of the deterministic optimization is to use quantum effects. It turns out that often, quantum annealing works much better than non-quantum one. Quantum annealing is the main technique behind the only commercially available computational devices that use quantum effects - D-Wave computers. The efficiency of quantum annealing depends on the proper selection of the annealing schedule, i.e., schedule that describes how the perturbations decrease with time. Empirically, it has been found that two schedules work best: power law and exponential ones. In this paper, we provide a theoretical explanation for these empirical successes, by proving that these two schedules are indeed optimal (in some reasonable sense).
引用
收藏
页码:963 / 967
页数:5
相关论文
共 50 条
  • [21] Beyond quantum annealing: optimal control solutions to maxcut problems
    Pecci, Giovanni
    Wang, Ruiyi
    Torta, Pietro
    Mbeng, Glen Bigan
    Santoro, Giuseppe
    QUANTUM SCIENCE AND TECHNOLOGY, 2024, 9 (04):
  • [22] Optimal quantum annealing: A variational shortcut-to-adiabaticity approach
    Passarelli, G.
    Fazio, R.
    Lucignano, P.
    PHYSICAL REVIEW A, 2022, 105 (02)
  • [23] Effects of strain on the optimal annealing temperature of GaInNAsSb quantum wells
    Yuen, HB
    Bank, SR
    Bae, H
    Wistey, MA
    Harris, JS
    APPLIED PHYSICS LETTERS, 2006, 88 (22)
  • [24] Quantum Speedup by Quantum Annealing
    Somma, Rolando D.
    Nagaj, Daniel
    Kieferova, Maria
    PHYSICAL REVIEW LETTERS, 2012, 109 (05)
  • [25] What is Simulated Annealing?
    Michael W. Trosset
    Optimization and Engineering, 2001, 2 : 201 - 213
  • [26] Comparative study of the performance of quantum annealing and simulated annealing
    Nishimori, Hidetoshi
    Tsuda, Junichi
    Knysh, Sergey
    PHYSICAL REVIEW E, 2015, 91 (01):
  • [27] What is Simulated Annealing?
    Trosset, Michael W.
    OPTIMIZATION AND ENGINEERING, 2001, 2 (02) : 201 - 213
  • [28] LEARNING QUANTUM ANNEALING
    Behrman, E. C.
    Steck, J. E.
    Moustafa, M. A.
    QUANTUM INFORMATION & COMPUTATION, 2017, 17 (5-6) : 469 - 487
  • [29] Pulsed Quantum Annealing
    Karanikolas, Vasilios
    Kawabata, Shiro
    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 2020, 89 (09)
  • [30] Quantum annealing: an overview
    Rajak, Atanu
    Suzuki, Sei
    Dutta, Amit
    Chakrabarti, Bikas. K. K.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2023, 381 (2241):