Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems

• URL: http://arxiv.org/abs/2502.05408v1
• Date: Sat, 08 Feb 2025 02:09:40 GMT
• Title: Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems
• Authors: Wenzheng Dong, Yuanlong Wang,
• Abstract summary: 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.
• Score: 0.6138671548064356
• License:
• Abstract: Precise qubit control in the presence of spatio-temporally 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 novel 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 non-perturbative nature of the non-Gaussian environment. This enables us to address certain non-perturbative 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-sized) open quantum dynamics but also highlight a resource-efficient approach for optimal control and possible error reduction techniques for current qubit devices.

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