Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
This work analyzes how well Quantum Circuit Born Machines (QCBMs) train when initialized with random Gaussian distributions. The authors provide mathematical bounds showing that gradient variance can be high and deviations from the mean can concentrate exponentially, helping to identify conditions leading to barren plateaus.