Supervised classifiers distinguish coherent from stochastic single-qubit noise on GST data, with near-perfect accuracy after feature engineering and margin-based robustness to sampling noise.
Errors and pseudo-thresholds for incoherent and coherent noise
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abstract
We compare the effect of single qubit incoherent and coherent errors on the logical error rate of the Steane [[7,1,3]] quantum error correction code by performing an exact full-density-matrix simulation of an error correction step. We find that the effective 1-qubit process matrix at the logical level reveals the key differences between the error models and provides insight into why the Pauli twirling approximation is a good approximation for incoherent errors and a poor approximation for coherent ones. Approximate channels composed of Clifford operations and Pauli measurement operators that are pessimistic at the physical level result in pessimistic error rates at the logical level. In addition, we observe that the pseudo-threshold can differ by a factor of five depending on whether the error is calculated using the fidelity or the distance.
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Classifying single-qubit noise using machine learning
Supervised classifiers distinguish coherent from stochastic single-qubit noise on GST data, with near-perfect accuracy after feature engineering and margin-based robustness to sampling noise.