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 Computational Science1(6), 403–409 (2021)
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Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.
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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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Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.