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

TL;DR

This paper introduces a model-independent method for calibrating flux crosstalk in superconducting circuits by leveraging their inherent periodic response, enabling scalable and efficient calibration for large quantum systems.

Contribution

The authors propose a novel flux crosstalk calibration technique based on periodicity, which does not rely on circuit models and is scalable to larger systems.

Findings

Achieved comparable calibration accuracy to existing methods on a quantum annealing circuit.

Demonstrated the method's effectiveness in a small-scale superconducting flux qubit system.

Found that the objective function landscape is nearly convex, facilitating optimization.

Abstract

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