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.
Comparing Experiments to the Fault-Tolerance Threshold
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abstract
Achieving error rates that meet or exceed the fault-tolerance threshold is a central goal for quantum computing experiments, and measuring these error rates using randomized benchmarking is now routine. However, direct comparison between measured error rates and thresholds is complicated by the fact that benchmarking estimates average error rates while thresholds reflect worst-case behavior when a gate is used as part of a large computation. These two measures of error can differ by orders of magnitude in the regime of interest. Here we facilitate comparison between the experimentally accessible average error rates and the worst-case quantities that arise in current threshold theorems by deriving relations between the two for a variety of physical noise sources. Our results indicate that it is coherent errors that lead to an enormous mismatch between average and worst case, and we quantify how well these errors must be controlled to ensure fair comparison between average error probabilities and fault-tolerance thresholds.
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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.