Quantum Optical Experiments Modeled by Long Short-Term Memory

Autor(en)

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

Abstrakt

We demonstrate how machine learning is able to model 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 the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Organisation(en)

Quantenoptik, Quantennanophysik und Quanteninformation

Externe Organisation(en)

Quantum Technol Labs qtlabs GmbH, University of Toronto, Vector Institute for Artificial Intelligence, Institute of advanced research in artifical intelligence, Johannes Kepler Universität Linz, Vienna Center for Quantum Science and Technology (VCQ), Österreichische Akademie der Wissenschaften (ÖAW)

Journal

Photonics

Band

8

Anzahl der Seiten

9

DOI

https://doi.org/10.3390/photonics8120535

Publikationsdatum

12-2021

Peer-reviewed

Ja

ÖFOS 2012

103026 Quantenoptik, 102019 Machine Learning

Schlagwörter

ASJC Scopus Sachgebiete

Instrumentation, Atomic and Molecular Physics, and Optics, Radiology Nuclear Medicine and imaging

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/bdf80fd2-a1e3-4b39-8354-65ab198f4f32