Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization

Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuning for each problem instance. We use a reinforcement learning agent in conjunction with a quantum-inspired algorithm to...

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Vydáno v:	Machine learning: science and technology Ročník 2; číslo 2; s. 25009 - 25020
Hlavní autoři:	Beloborodov, Dmitrii, Ulanov, A E, Foerster, Jakob N, Whiteson, Shimon, Lvovsky, A I
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bristol IOP Publishing 01.06.2021
Témata:	Algorithms Combinatorial analysis combinatorial optimization Ising model Machine learning maximum cut Optimization reinforcement learning Training Tuning
ISSN:	2632-2153, 2632-2153
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Abstract	Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuning for each problem instance. We use a reinforcement learning agent in conjunction with a quantum-inspired algorithm to solve the Ising energy minimization problem, which is equivalent to the Maximum Cut problem. The agent controls the algorithm by tuning one of its parameters with the goal of improving recently seen solutions. We propose a new Rescaled Ranked Reward (R3) method that enables a stable single-player version of self-play training and helps the agent escape local optima. The training on any problem instance can be accelerated by applying transfer learning from an agent trained on randomly generated problems. Our approach allows sampling high quality solutions to the Ising problem with high probability and outperforms both baseline heuristics and a black-box hyperparameter optimization approach.
AbstractList	Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuning for each problem instance. We use a reinforcement learning agent in conjunction with a quantum-inspired algorithm to solve the Ising energy minimization problem, which is equivalent to the Maximum Cut problem. The agent controls the algorithm by tuning one of its parameters with the goal of improving recently seen solutions. We propose a new Rescaled Ranked Reward (R3) method that enables a stable single-player version of self-play training and helps the agent escape local optima. The training on any problem instance can be accelerated by applying transfer learning from an agent trained on randomly generated problems. Our approach allows sampling high quality solutions to the Ising problem with high probability and outperforms both baseline heuristics and a black-box hyperparameter optimization approach.
Author	Ulanov, A E Whiteson, Shimon Lvovsky, A I Beloborodov, Dmitrii Foerster, Jakob N
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Cites_doi	10.1103/PhysRevLett.122.040607 10.1038/srep00571 10.1088/0305-4470/15/10/028 doi.org/10.1007/s42484-019-00001-w 10.1126/science.aah5178 10.1109/WACV.2017.58 10.1016/j.engappai.2012.09.001 10.1126/science.3755256 10.1002/9781118593486.ch4 10.1126/science.220.4598.671 10.1609/aaai.v34i03.5616 10.1364/OE.27.010288 10.1109/MC.2019.2908836 10.1126/science.aah4243 10.1162/106365603321828970
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References	22 Feurer M (9) 2018 23 24 27 Farhi E (8) 2014 Barahona F (2) 1982; 15 Ye Y (36) 2003 Dumoulin V (6) 2016 King A D (16) 2018 Xinyun C (35) 2018 31 Abe K (1) 2019 11 33 Barrett T D (3) 2019 12 Smith L N (30) 2017 13 14 Mirhoseini A (25) 2017 Erdős P (7) 1960; 5 17 Kool W (18) 2018 Ulanov A E (32) 2019 Cplex optimizer (5) 2019 Vinyals O (34) 2015 Laterre A (19) 2018 Mittal A (26) 2019 4 Khalil E (15) 2017 Li Z (21) 2018 Gurobi optimization (10) 2019 Schulman J (28) 2017 Silver D (29) 2017 20
References_xml	– year: 2019 ident: 3 – ident: 20 doi: 10.1103/PhysRevLett.122.040607 – start-page: 3 year: 2018 ident: 9 publication-title: Automatic Machine Learning: Methods, Systems, Challenges – ident: 27 doi: 10.1038/srep00571 – volume: 15 start-page: 3241 issn: 0305-4470 year: 1982 ident: 2 publication-title: J. Phys. A: Math. Gen. doi: 10.1088/0305-4470/15/10/028 – year: 2017 ident: 29 – year: 2019 ident: 32 – year: 2017 ident: 28 – ident: 33 doi: doi.org/10.1007/s42484-019-00001-w – year: 2019 ident: 5 – ident: 24 doi: 10.1126/science.aah5178 – start-page: 464 year: 2017 ident: 30 publication-title: 2017 IEEE Conf. on Applications of Computer Vision (WACV) doi: 10.1109/WACV.2017.58 – year: 2014 ident: 8 – ident: 4 doi: 10.1016/j.engappai.2012.09.001 – start-page: 539 year: 2018 ident: 21 publication-title: Advances in Neural Information Processing Systems – year: 2016 ident: 6 – ident: 12 doi: 10.1126/science.3755256 – year: 2018 ident: 16 – year: 2003 ident: 36 – start-page: 2692 issn: 1049-5258 year: 2015 ident: 34 publication-title: Adv. Neural Inf. Process. Syst. – year: 2018 ident: 19 – ident: 22 doi: 10.1002/9781118593486.ch4 – ident: 17 doi: 10.1126/science.220.4598.671 – ident: 14 doi: 10.1609/aaai.v34i03.5616 – year: 2018 ident: 18 – ident: 31 doi: 10.1364/OE.27.010288 – year: 2019 ident: 1 – ident: 23 doi: 10.1109/MC.2019.2908836 – year: 2018 ident: 35 – start-page: 6348 year: 2017 ident: 15 publication-title: Advances in Neural Information Processing Systems – year: 2019 ident: 10 – ident: 13 doi: 10.1126/science.aah4243 – year: 2019 ident: 26 – ident: 11 doi: 10.1162/106365603321828970 – volume: 5 start-page: 17 year: 1960 ident: 7 publication-title: Publ. Math. Inst. Hung. Acad. Sci – start-page: 2430 year: 2017 ident: 25 publication-title: Proc. of the 34th Int. Conf. on Machine Learning-Volume 70
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SubjectTerms	Algorithms Combinatorial analysis combinatorial optimization Ising model Machine learning maximum cut Optimization reinforcement learning Training Tuning
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Title	Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization
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