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Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization

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 see...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/2632-2153/abc328

Authors


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Role:
Author
ORCID:
0000-0003-2211-559X
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Sub department:
Atomic & Laser Physics
Oxford college:
Keble College
Role:
Author
Publisher:
IOP Publishing Publisher's website
Journal:
Machine Learning: Science and Technology Journal website
Volume:
2
Issue:
2
Article number:
025009
Publication date:
2020-12-29
Acceptance date:
2020-10-20
DOI:
EISSN:
2632-2153
Language:
English
Keywords:
Pubs id:
1089222
Local pid:
pubs:1089222
Deposit date:
2021-01-19

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