Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
Qubit readout error mitigation with bit-flip averaging
3 Pith papers cite this work, alongside 51 external citations. Polarity classification is still indexing.
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quant-ph 3years
2026 3verdicts
UNVERDICTED 3roles
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A branch-resolved framework using classical Choi shadows characterizes feed-forward errors in dynamic circuit teleportation, showing reversal in post-processing vs. PROM mitigation performance between qubit layouts with different readout errors.
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
citing papers explorer
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Single-shot quantum neural networks with amplitude estimation
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
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Branch-Resolved Characterization of Feed-Forward Error in Dynamic Teleportation via Classical Choi Shadows
A branch-resolved framework using classical Choi shadows characterizes feed-forward errors in dynamic circuit teleportation, showing reversal in post-processing vs. PROM mitigation performance between qubit layouts with different readout errors.
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MCMit: Mid-Circuit Measurement Error Mitigation
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.