The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks

ArXi:2504.19239v2 Announce Type: replace-cross Quantum neural networks hold promise for tackling computationally challenging tasks that are intractable for classical computers. However, their practical application is hindered by significant optimization challenges, arising from complex loss landscapes characterized by barren plateaus and numerous local minima. These problems become severe as the number of parameters or qubits increases, hampering effective