Deep Bayesian Experimental Design Characterizes Large-scale Quantum Systems

Researchers have recently demonstrated how machine learning can be used to implement the deep Bayesian experimental design of large-scale quantum networks. Bayesian experimental design is a technique for automatically identifying the most useful experiments that will allow us to understand as much as possible about a physical system. This technique is known to be computationally very expensive, but with the development of artificial intelligence techniques such as neural networks, it is now possible to make it more feasible.