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Estimating the coherence of noise in quantum control of a solid-state qubit
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To exploit a given physical system for quantum information processing, it is critical to understand the different types of noise affecting quantum control. Distinguishing coherent and incoherent errors is extremely useful as they can be reduced in different ways. Coherent errors are generally easier to reduce at the hardware level, e.g. by improving calibration, whereas some sources of incoherent errors, e.g. T2* processes, can be reduced by engineering robust pulses. In this work, we illustrate how purity benchmarking and randomized benchmarking can be used together to distinguish between coherent and incoherent errors and to quantify the reduction in both of them due to using optimal control pulses and accounting for the transfer function in an electron spin resonance system. We also prove that purity benchmarking provides bounds on the optimal fidelity and diamond norm that can be achieved by correcting the coherent errors through improving calibration.
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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.
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