Quantum Optical Experiments Modeled by Long Short-Term Memory

TL;DR

This paper shows that LSTM neural networks can effectively model complex quantum experiments, enabling faster search and automated design of multiparticle high-dimensional quantum states, which is crucial for advancing quantum technologies.

Contribution

It introduces the use of LSTM neural networks to model quantum experiments, reducing computational effort and facilitating automated experiment design.

Findings

LSTM models accurately predict quantum output states.

Machine learning improves search efficiency for quantum experiments.

Potential for automated quantum experiment generation.

Abstract

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 automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.