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Research questionHow can quantum neural networks prevent simulator-era postprocessing from silently discarding hardware measurements?Postprocessing can silently discard valid hardware measurement shots when it interprets physical-qubit bit-strings using simulator-era assumptions. The resulting unnormalised probabilities can distort predictions and weaken training signals without an API warning.
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Latest papersRecent research connected to this question, newest first.Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural NetworksThe evidence comes from a case study of Qiskit Machine Learning’s SamplerQNN on two IBM quantum backends. Runs involved bit-strings spanning more than 100 physical qubits; the reported filter discarded 85–99.6% of valid shots, reducing inference accuracy from 0.94 to 0.39 and compressing training loss signals by 22–27× across library versions 0.8.4–0.9.0. A layout-based marginalisation fix was implemented in the codebase, but the evidence is limited to the reported experiments and systems.research paper · Sep 4, 2026
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