System Optimization of Gate Frequencies for Surface Code Quantum Error Correction

A critical component of operating quantum processors is mitigating computational errors from energy-relaxation, dephasing, leakage, and control inaccuracies. In superconducting qubits, these sources of error can arise from control-electronics noise, control-pulse distortions, parasitic coupling between computational elements, and more. In frequency-tunable qubit architectures, it’s possible to mitigate these sources of error by choreographing qubit gate frequencies over the course of quantum algorithms. This choreography maps to constructing and optimizing a high-dimensional, high-constraint, non-convex, and time-dependent objective over an astronomical search space. In this talk, I will introduce the frequency optimization problem and the Snake optimizer [1, 2] that we developed to solve it for Google’s flagship quantum processors. Finally, I will discuss the problem in the context of our team’s distance-5 surface code quantum error correction demonstration on 49 qubits of a Sycamore processor [3].

[1] Calibration of quantum processor operator parameters, P. V. Klimov, US20200387822A1 (Filed 2019).

[2] The Snake Optimizer for Learning Quantum Processor Control Parameters, P. V. Klimov, J. Kelly, J. M. Martinis, H. Neven, arxiv:2006.04594 (2020).

[3] Suppressing quantum errors by scaling a surface code logical qubit, Google Quantum AI, arXiv:2207.06431 (2022).