Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
Bonet-Monroig, R
4 Pith papers cite this work, alongside 256 external citations. Polarity classification is still indexing.
4
Pith papers citing it
256
external citations · OpenAlex
fields
quant-ph 4years
2026 4representative citing papers
Mid-circuit stabilizer verification in six-qubit GSE-encoded Clifford Trotter steps reduces logical error rates by up to 54% on Barium ion hardware, with the gain vanishing if checks are deferred to circuit end.
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
citing papers explorer
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Certified Finite-Shot Operating Windows for Virtual Distillation and Symmetry Verification
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
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Mid-Circuit Measurements for Clifford Noise Reduction in Hamiltonian Simulations
Mid-circuit stabilizer verification in six-qubit GSE-encoded Clifford Trotter steps reduces logical error rates by up to 54% on Barium ion hardware, with the gain vanishing if checks are deferred to circuit end.
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GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
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When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.