Machine learning (ML) has become an attractive tool for solving various problems in different fields of physics, including the quantum domain. Here, we show how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states and delivers successful action sequences that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL for quantum protocols that deal with a non-trivial continuous state space, and discuss the scaling of these approaches for the preparation of larger entangled (cluster) states.
%0 Journal Article
%1 Mackeprang2020
%A Mackeprang, Jelena
%A Dasari, Durga B. Rao
%A Wrachtrup, Jörg
%D 2020
%J Quantum Machine Intelligence
%K pi3 wrachtrup
%N 1
%P 1--14
%R 10.1007/s42484-020-00016-8
%T A reinforcement learning approach for quantum state engineering
%U https://doi.org/10.1007/s42484-020-00016-8
%V 2
%X Machine learning (ML) has become an attractive tool for solving various problems in different fields of physics, including the quantum domain. Here, we show how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states and delivers successful action sequences that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL for quantum protocols that deal with a non-trivial continuous state space, and discuss the scaling of these approaches for the preparation of larger entangled (cluster) states.
@article{Mackeprang2020,
abstract = {Machine learning (ML) has become an attractive tool for solving various problems in different fields of physics, including the quantum domain. Here, we show how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states and delivers successful action sequences that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL for quantum protocols that deal with a non-trivial continuous state space, and discuss the scaling of these approaches for the preparation of larger entangled (cluster) states.},
added-at = {2020-06-03T09:56:15.000+0200},
author = {Mackeprang, Jelena and Dasari, Durga B. Rao and Wrachtrup, J{\"o}rg},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2371bd3421e5b2c305f03e913555fc190/shirschmann},
day = 01,
doi = {10.1007/s42484-020-00016-8},
interhash = {361958962ead7149b4a18bd390ac986a},
intrahash = {371bd3421e5b2c305f03e913555fc190},
issn = {2524-4914},
journal = {Quantum Machine Intelligence},
keywords = {pi3 wrachtrup},
month = jun,
number = 1,
pages = {1--14},
timestamp = {2020-06-03T07:56:15.000+0200},
title = {A reinforcement learning approach for quantum state engineering},
url = {https://doi.org/10.1007/s42484-020-00016-8},
volume = 2,
year = 2020
}
