Quantum computing promises to improve our ability to perform some critical computational tasks in the future. Machine learning is changing the way we use computers in our present everyday life and in science. It is natural to seek connections between these two emerging approaches to computing, in the hope of reaping multiple benefits. The search for connecting links has just started, but we are already seeing a lot of potential in this wild, unexplored territory. We present here two new research articles: "Precise measurement of quantum observables with neural-network estimators," published in Physical Review Research, and "Fermionic neural-network states for ab-initio electronic structure," published in Nature Communications.
