Photonics, Volume 8, Issue 12
2021 December - 70 articles
Cover Story: Quantum entanglement is a cornerstone of upcoming quantum technologies such as quantum computation and quantum cryptography. The topics of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. As it is usually impossible to reconstruct an experimental setup that produces such states, searching for interesting experiments requires the random simulation of millions of setups and calculation of the respective output states. In this work, we show that machine learning models can provide significant improvements over random searching, since an LSTM neural network can learn to model quantum experiments by predicting output state characteristics for given setups. This approach not only allows for a faster search, but is an essential step towards the automated design of multiparticle, high-dimensional quantum experiments. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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