Reinforcement learning assisted recursive QAOA

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constrai...

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Veröffentlicht in:EPJ quantum technology Jg. 11; H. 1; S. 6
Hauptverfasser: Patel, Yash J., Jerbi, Sofiene, Bäck, Thomas, Dunjko, Vedran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
Schlagworte:
Algorithms
Combinatorial analysis
Design optimization
Ising model
Nanotechnology and Microengineering
Optimization
Physics
Physics and Astronomy
Quantum Information Technology
Quantum Physics
Spintronics
Quantum computing
Quantum approximate optimization algorithm
Combinatorial optimization
Reinforcement learning
ISSN:2662-4400, 2196-0763, 2196-0763
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Zusammenfassung:In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.
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ISSN:2662-4400
2196-0763
2196-0763
DOI:10.1140/epjqt/s40507-023-00214-w