Estimating the Error Budgets of Quantum Gates in Superconducting Qubits

There is a variety of different error mechanisms present in current superconducting based quantum computers and distinguishing between their contributions can prove to be challenging. However, an accurate estimate of the main error source contributions is a valuable resource which enables the efficient selection of the appropriate calibration or mitigation procedures.

There is a wide variety of different quantum characterization verification and validation (QCVV) techniques currently available (e.g. [1,2]), but most of them are based on various assumptions about the error mechanisms, most commonly the Markovianity, which may not always be justified in superconducting qubits [3]. Moreover, interpreting the results of QCVV techniques and connecting them to specific physical error mechanisms is not straightforward [4].

As an alternative, we have developed an approach tailored to superconducting quantum hardware, based on detailed simulations of the relevant noise processes and Bayesian regression. While in our previous work [5] we used a similar approach to extract environment properties and models, here the aim is to predict the infidelity contributions. Our procedure also estimates the prediction uncertainties, which may arise due to out-of-model effects.

[1] E. Nielsen, et al., Quantum 5, 557 (2021).

[2] E. Knill, et al., PRA 77, 012307 (2008).

[3] H. Zhang, et al., PRAppl. 17, 054018 (2022).

[4] R. Blume-Kohout, et al., PRX Quantum 3, 020335 (2022).

[5] M. Papic and I. de Vega, PRA 105, 022605 (2022).