A constant-depth, parallelizable protocol using repeated non-destructive single-qubit measurements can separately characterize state-preparation and measurement errors.
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LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.
Derives closed-form intervals for compatible population changes from calibrated qubit readouts and identifies cases where stable data permit multiple population interpretations.
citing papers explorer
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Separate and efficient characterization of state-preparation and measurement errors using single-qubit operations
A constant-depth, parallelizable protocol using repeated non-destructive single-qubit measurements can separately characterize state-preparation and measurement errors.
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Learning Lindblad Dynamics of a Superconducting Quantum Processor
LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
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Non-Clifford Benchmarking via Ensemble Feature Selection
Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.
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Stable Qubit Readout and the Identifiability of Population Change
Derives closed-form intervals for compatible population changes from calibrated qubit readouts and identifies cases where stable data permit multiple population interpretations.