Some problems in physics can be handled only after a suitable ansatz solution has been guessed, proving to be resilient to generalization. The coherent transport of a quantum state by adiabatic passage through an array of semiconductor quantum dots is an excellent example of such a problem, where it is necessary to introduce a so-called counterintuitive control sequence. Instead, the deep reinforcement learning (DRL) technique has proven to be able to solve very complex sequential decision-making problems, despite a lack of prior knowledge. We show that DRL discovers a control sequence that outperforms the counterintuitive control sequence. DRL can even discover novel strategies when realistic disturbances affect an ideal system, such as detuning or when dephasing or losses are added to the master equation. DRL is effective in controlling the dynamics of quantum states and, more generally, whenever an ansatz solution is unknown or insufficient to effectively treat the problem.

Coherent transport of quantum states by deep reinforcement learning / R. Porotti, D. Tamascelli, M. Restelli, E. Prati. - In: COMMUNICATIONS PHYSICS. - ISSN 2399-3650. - 2(2019), pp. 61.1-61.9.

Coherent transport of quantum states by deep reinforcement learning

D. Tamascelli;
2019

Abstract

Some problems in physics can be handled only after a suitable ansatz solution has been guessed, proving to be resilient to generalization. The coherent transport of a quantum state by adiabatic passage through an array of semiconductor quantum dots is an excellent example of such a problem, where it is necessary to introduce a so-called counterintuitive control sequence. Instead, the deep reinforcement learning (DRL) technique has proven to be able to solve very complex sequential decision-making problems, despite a lack of prior knowledge. We show that DRL discovers a control sequence that outperforms the counterintuitive control sequence. DRL can even discover novel strategies when realistic disturbances affect an ideal system, such as detuning or when dephasing or losses are added to the master equation. DRL is effective in controlling the dynamics of quantum states and, more generally, whenever an ansatz solution is unknown or insufficient to effectively treat the problem.
Reinforcement learning; quantum transport; quantum dots; open quantum systems
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/651386
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