Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

• URL: http://arxiv.org/abs/2303.03347v3
• Date: Wed, 6 Sep 2023 01:17:44 GMT
• Title: Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays
• Authors: Cora N. Barrett, Amir H. Karamlou, Sarah E. Muschinske, Ilan T. Rosen, Jochen Braum\"uller, Rabindra Das, David K. Kim, Bethany M. Niedzielski, Meghan Schuldt, Kyle Serniak, Mollie E. Schwartz, Jonilyn L. Yoder, Terry P. Orlando, Simon Gustavsson, Jeffrey A. Grover, William D. Oliver
• Abstract summary: Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. Magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies. We introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits.
• Score: 0.1950600573389501
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling with system size, while maintaining a median qubit frequency error below $300$ kHz.

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