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.
Nature communications 9(1), 4812 (2018)
2 Pith papers cite this work. Polarity classification is still indexing.
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Distributed QNNs with partitioned feature encoding achieve >96% accuracy on MNIST 10-class classification using ensembles of small circuits.
citing papers explorer
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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.
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Distributed Quantum Neural Networks via Partitioned Features Encoding
Distributed QNNs with partitioned feature encoding achieve >96% accuracy on MNIST 10-class classification using ensembles of small circuits.