Quantum Optical Experiments Modeled by Long Short-Term Memory

被引:7
|
作者
Adler, Thomas [1 ]
Erhard, Manuel [2 ,3 ,8 ]
Krenn, Mario [4 ,5 ,6 ,9 ]
Brandstetter, Johannes [1 ,10 ]
Kofler, Johannes [1 ]
Hochreiter, Sepp [1 ,7 ]
机构
[1] Johannes Kepler Univ Linz, Inst Machine Learning, ELLIS Unit Linz, LIT AI Lab, A-4040 Linz, Austria
[2] Univ Vienna, Austrian Acad Sci, Inst Quantum Opt & Quantum Informat, A-1090 Vienna, Austria
[3] Univ Vienna, Vienna Ctr Quantum Sci & Technol, A-1090 Vienna, Austria
[4] Univ Toronto, Dept Chem, Toronto, ON M5G 1M1, Canada
[5] Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada
[6] Univ Toronto, Dept Comp Sci, Toronto, ON M5G 1M1, Canada
[7] Inst Adv Res Artificial Intelligence IARAI, Landstrasser Hauptstr 5, A-1030 Vienna, Austria
[8] Quantum Technol Labs GmbH, Wohllebengasse 4-4, A-1040 Vienna, Austria
[9] Max Planck Inst Sci Light, D-91058 Erlangen, Germany
[10] Univ Amsterdam, Fac Sci, Informat Inst, NL-1090 GH Amsterdam, Netherlands
基金
奥地利科学基金会;
关键词
quantum optics; multipartite high-dimensional entanglement; supervised machine learning; long short-term memory; ENTANGLEMENT;
D O I
10.3390/photonics8120535
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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.
引用
收藏
页数:9
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