Learning to Calibrate Quantum Control Pulses by Iterative Deconvolution

In experimental control of quantum systems, the precision is often hindered by imperfect applied electronics that distort pulses delivered to target quantum devices. To mitigate such error, the deconvolution method is commonly used for compensating the distortion via a convolutional model. However, its effectiveness is limited by model inaccuracies (e.g., imprecise parameters or unmodeled distortion dynamics). In this article, we propose a learning-based scheme to eliminate the residual calibration error by repeatedly applying the deconvolution operations. The resulting iterative deconvolution method is shown by simulation examples to be able to correct both linear and nonlinear model errors to the highest precision allowed by available finite sampling rates, and the intersampling error caused by finite sampling rate can be suppressed by actively introducing nonlinear components in the control electronics. The proposed method is also experimentally applied on a superconducting platform, which demonstrates improved performance than the noniterative deconvolution methods.