Increasing the number of local patches in a distributed quantum neural network architecture reduces the largest Hessian eigenvalue at minima and introduces a class-dependent outlier structure in the eigenspectrum.
Quantum Machine Intelligence 5(2), 23 (2023)
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The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks
Increasing the number of local patches in a distributed quantum neural network architecture reduces the largest Hessian eigenvalue at minima and introduces a class-dependent outlier structure in the eigenspectrum.