The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M)
affect the loss landscape is crucial to designing scalable QNN architectures. Here we
rigorously analyze the overparametrization phenomenon in QNNs, defining
overparametrization as the regime where the QNN has more than a critical number of
parameters M c allowing it to explore all relevant directions in state space. Our main results …