Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, and Sepp Hochreiter

We demonstrate how machine learning helps to design experiments in quantum
physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest
are complex quantum states with more than two particles and a large number of
entangled quantum levels. Given such a multiparticle high-dimensional quantum
state, it is usually impossible to reconstruct an experimental setup that produces
it. To search for interesting experiments, one thus has to randomly create millions
of setups on a computer and calculate the respective output states. In this work,
we show that machine learning models can provide significant improvement over
random search. We demonstrate that a long short-term memory (LSTM) neural
network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing
the states themselves. This approach not only allows for faster search but is also an
essential step towards automated design of multiparticle high-dimensional quantum
experiments using generative machine learning models.

Machine Learning and the Physical Sciences Workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019.

View paper

IARAI Authors
Dr Sepp Hochreiter

Deep Learning, LSTMs, Quantum Physics


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