Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems
Wenzheng Dong, Yuanlong Wang, Muhammad Qasim Khan · Physical Review A · 2025
Precise qubit control in the presence of spatiotemporally correlated noise is pivotal for transitioning to fault-tolerant quantum computing. Generically, such noise can also have non-Gaussian statistics, which hampers existing non-Markovian noise spectroscopy protocols. By utilizing frame-based characterization and a control-based symmetry analysis, we show how to achieve higher-order spectral estimation for noise-optimized circuit design. Remarkably, we find that the digitally driven qubit dynamics can be solely determined by the complexity of the applied control rather than the nonperturbative nature of the non-Gaussian environment. This enables us to address certain nonperturbative qubit dynamics more simply. We delineate several complexity bounds for learning such high-complexity noise and demonstrate our single- and two-qubit digital characterization and control using a series of numerical simulations. Our results not only provide insights into the exact solvability of (small-size) open quantum dynamics but also highlight a resource-efficient approach for optimal control and possible error-reduction techniques for current qubit devices.