Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
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The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.