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

• URL: http://arxiv.org/abs/2211.01497v2
• Date: Sun, 4 Feb 2024 00:39:51 GMT
• Title: Optimizing for periodicity: a model-independent approach to flux crosstalk calibration for superconducting circuits
• Authors: X. Dai, R. Trappen, R. Yang, S. M. Disseler, J. I. Basham, J. Gibson, A. J. Melville, B. M. Niedzielski, R. Das, D. K. Kim, J. L. Yoder, S. J. Weber, C. F. Hirjibehedin, D. A. Lidar, and A. Lupascu
• Abstract summary: Large-scale quantum computers based on flux-tunable superconducting circuits face the problem of flux crosstalk. We propose a new method for calibrating flux crosstalk, which is independent of the underlying circuit model. We demonstrate this method on a small-scale quantum annealing circuit based on superconducting flux qubits.
• Score: 0.2236338337974111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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