Optimizing Quantum Gate Frequencies for Google’s Quantum Processors

A crucial component of operating a quantum processor is mitigating computational errors from energy-relaxation, dephasing, leakage, and control imperfections. In superconducting qubits, these sources of error can arise from control-electronics noise, control-pulse distortions, and the parasitic coupling of qubits to other qubits, two-level system defects, spurious microwave modes, and the control and readout circuitry. In frequency-tunable qubit architectures, it is 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 a search space that significantly exceeds the Hilbert-space dimension of the processor. In this talk, I will introduce the frequency optimization problem and the Snake optimizer that we developed to solve it for Google’s flagship quantum processors [1].

[1] Quantum supremacy using a programmable superconducting processor, Google AI Quantum and collaborators. Nature 574, 505-510 (2019).