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Flux tunability is an important engineering resource for superconductingcircuits. Large-scale quantum computers based on flux-tunable superconductingcircuits face the problem of flux crosstalk, which needs to be accuratelycalibrated to realize high-fidelity quantum operations. Typical calibrationmethods either assume that circuit elements can be effectively decoupled andsimple models can be applied, or require a large amount of data. Such methodsbecome ineffective as the system size increases and circuit interactions becomestronger. Here we propose a new method for calibrating flux crosstalk, which isindependent of the underlying circuit model. Using the fundamental propertythat superconducting circuits respond periodically to external fluxes,crosstalk calibration of N flux channels can be treated as N independentoptimization problems, with the objective functions being the periodicity of ameasured signal depending on the compensation parameters. We demonstrate thismethod on a small-scale quantum annealing circuit based on superconducting fluxqubits, achieving comparable accuracy with previous methods. We also show thatthe objective function usually has a nearly convex landscape, allowingefficient optimization.

Optimizing for periodicity: a model-independent approach to flux crosstalk calibration for superconducting circuits