We propose a reinforcement learning (RL) scheme for feedback quantum control within the quantum approximate optimization algorithm (QAOA). We reformulate the QAOA variational minimization as a learning task, where an RL agent chooses the control parameters for the unitaries, given partial information on the system. Such an RL scheme finds a policy converging to the optimal adiabatic solution of the quantum Ising chain that can also be successfully transferred between systems with different sizes, even in the presence of disorder. This allows for immediate experimental verification of our proposal on more complicated models: The RL agent is trained on a small control system, simulated on classical hardware, and then tested on a larger physical sample.

Reinforcement-learning-assisted quantum optimization / Wauters, M. M.; Panizon, E.; Mbeng, G. B.; Santoro, G. E.. - In: PHYSICAL REVIEW RESEARCH. - ISSN 2643-1564. - 2:3(2020), pp. 1-8. [10.1103/PhysRevResearch.2.033446]

Reinforcement-learning-assisted quantum optimization

Wauters, M. M.;Panizon, E.;Mbeng, G. B.;Santoro, G. E.
2020-01-01

Abstract

We propose a reinforcement learning (RL) scheme for feedback quantum control within the quantum approximate optimization algorithm (QAOA). We reformulate the QAOA variational minimization as a learning task, where an RL agent chooses the control parameters for the unitaries, given partial information on the system. Such an RL scheme finds a policy converging to the optimal adiabatic solution of the quantum Ising chain that can also be successfully transferred between systems with different sizes, even in the presence of disorder. This allows for immediate experimental verification of our proposal on more complicated models: The RL agent is trained on a small control system, simulated on classical hardware, and then tested on a larger physical sample.
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https://doi.org/10.1103/PhysRevResearch.2.033446
https://arxiv.org/abs/2004.12323
Wauters, M. M.; Panizon, E.; Mbeng, G. B.; Santoro, G. E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/126718
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