Unified evolutionary optimization for high-fidelity spin qubit operations

Abstract

Developing optimal strategies to calibrate quantum processors for high-fidelity operation is one of the outstanding challenges in quantum computing today. Here, we demonstrate multiple examples of high-fidelity operations achieved using a unified global optimization-driven automated calibration routine on a six dot semiconductor quantum processor. Within the same algorithmic framework we optimize readout, shuttling and single-qubit quantum gates by tailoring task-specific cost functions and tuning parameters based on the underlying physics of each operation. Our approach reaches systematically $99\%$ readout fidelity, $>99\%$ shuttling fidelity over an effective distance of 10$\hat{I}¼$m, and $>99.5\%$ single-qubit gate fidelity on timescales similar or shorter compared to those of expert human operators. The flexibility of our gradient-free closed loop algorithmic procedure allows for seamless application across diverse qubit functionalities while providing a systematic framework to tune-up semiconductor quantum devices and enabling interpretability of the identified optimal operation points.

13 pages, 10 figures, code https://gitlab.com/QMAI/papers/evolutionarytuning