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.
Highlights
Some problems in physics can be handled only after a suitable ansatz solution has been guessed, proving to be resilient to generalization
Reinforcement learning (RL) is a set of techniques used to learn behavior in sequential decision-making problems when no prior knowledge about the system dynamics is available or when the control problem is too complex for classical optimal-control algorithms
Contrary to the employment of special ansatz solutions, we have shown that deep reinforcement learning (DRL) discovers novel sequences of control operations to achieve a target state, regardless of the possible deviations from the ideal conditions
Summary
Some problems in physics can be handled only after a suitable ansatz solution has been guessed, proving to be resilient to generalization. We demonstrate that DRL implemented in a compact neural network can, first of all, autonomously discover an analog of the counterintuitive gate pulse sequence without any prior knowledge, finding a control path in a problem whose solution is far from the equilibrium of the initial conditions. This method can outperform the previously introduced analytical solutions in terms of processing speed and when the system deviates from ideal conditions, which are here represented by the imperfect tuning of the ground states of the quantum dots, dephasing and losses. As a further advantage of such an approach, a 2-step temporal Bayesian network (2TBN) analysis can identify which parameters of the system influence the process to a greater extent
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
