Tunable coupling architectures consisting of frequency-tunable qubits and couplers are widely employed in superconducting quantum processors. Magnetic flux crosstalk between these components poses a significant challenge to platform scalability, while quantum crosstalk induced by strong qubit-coupler interactions further complicates the compensation of flux crosstalk. To address these issues, we propose and experimentally validate a spin-echo-based method that effectively separates quantum and flux crosstalk, enabling accurate characterization of flux crosstalk. Furthermore, we integrate learning-based algorithm with a high-parallelism measurement scheme to improve efficiency. This approach achieves the stabilization of frequency-shift fluctuations at a noise baseline of approximately 20 kHz, with the accuracy of the crosstalk coefficient reaching an order of ${10}^{-5}$ after compensation. The method provides a robust and efficient framework for mitigating crosstalk, paving the way for high-fidelity control of large-scale quantum processors.

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• Optoelectronics
• Quantum circuits
• Quantum control
• Quantum error correction
• Quantum gates
• Quantum information with solid state qubits
• Superconducting qubits
• Microwave techniques