Researchers have developed a measurement-induced quantum neural network (MINN), a novel architecture that leverages mid-circuit measurement outcomes to determine entangling gates in subsequent layers. This approach differs from traditional monitored circuits, where sites and gates are randomly sampled, by utilizing parametrized and variational gates that produce correlated history-dependent dynamics. The MINN architecture enables the injection of non-trivial correlations, allowing for more complex and adaptive quantum processing. This breakthrough has significant implications for the field of quantum computing, as it potentially enables the creation of more sophisticated and dynamic quantum models1. The ability to adapt and respond to measurement outcomes in real-time could lead to significant advances in quantum machine learning and simulation. So what matters to practitioners is that this innovation could pave the way for more efficient and powerful quantum computing systems, with potential applications in fields such as cryptography and optimization.