Quantum Optical Experiments Modeled by Long Short-Term Memory
This work addresses a bottleneck in quantum physics for researchers designing experiments, offering an incremental improvement over random search methods.
The paper tackled the problem of designing quantum experiments for complex multiparticle high-dimensional entangled states, which typically requires random search of millions of setups, and demonstrated that an LSTM neural network can model these experiments to predict output states without full computation, enabling faster search.
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.