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Estimating detector error models from syndrome data
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Estimating detector error models from syndrome data
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Protecting quantum information using quantum error correction (QEC) requires repeatedly measuring stabilizers to extract error syndromes that are used to identify and correct errors. Syndrome extraction data provides information about the processes that cause errors. The collective effects of these processes can be described by a detector error model (DEM). We show how to estimate probabilities of individual DEM events, and of aggregated classes of DEM events, using data from multiple cycles of syndrome extraction.
Forward citations
Cited by 5 Pith papers
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QMCtwin: Master-Equation Simulation of Syndrome Statistics Beyond Pauli Noise
QMCtwin simulates master-equation syndrome statistics for a distance-7 surface code and reveals biases and correlations absent in Pauli-twirled models.
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A digitally controlled silicon quantum processing unit
An integrated silicon quantum processing unit with digital cryogenic control demonstrates substantially improved exchange-only qubit operations and implements early quantum error correction codes.
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Plaquette: A hardware-aware design platform for fault-tolerant quantum computers
Plaquette compiles realistic quantum hardware noise models into multiple sampler representations, showing that Pauli-twirled approximations can misestimate logical error rates by an order of magnitude compared to leak...
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Reconstruction of detector error model for quantum error correction
CAHR inverts experimental syndrome statistics into discrete physical hypergraphs for surface and color codes without false positives.
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Logical error estimation from syndrome data of surface-code experiments
Estimating DEM probabilities from experimental syndromes improves logical error rates by 5-10% in surface-code memory experiments on Google Willow and IBM ibm_miami without additional circuits or supervised fitting.
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