Quantum machine learning algorithms, like their classical counterparts, can get lost in a training landscape. A new proof by Los Alamos National Laboratory scientists shows that a technique called overparametrization enables quantum machine learning algorithms to find the highest point in the landscape — the solution to a problem — without getting stuck on false peaks.