Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks
A layout-based marginalisation fix is implemented, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.
Soraya V. Panambalom, Edoardo Altamura, Nicholas Chancellor et al.
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