Journal article
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization
- Abstract:
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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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(Version of record, 746.7KB)
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- Publisher copy:
- 10.1088/2632-2153/abc328
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Authors
Bibliographic Details
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1089222
- Local pid:
- pubs:1089222
- Deposit date:
- 2021-01-19
Terms of use
- Copyright holder:
- Beloborodov et al.
- Copyright date:
- 2020
- Rights statement:
- © 2020 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
- Licence:
- CC Attribution (CC BY)
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