  High performance quantum computing requires a calibration system that learns
optimal control parameters much faster than system drift. In some cases, the
learning procedure requires solving complex optimization problems that are
non-convex, high-dimensional, highly constrained, and have astronomical search
spaces. Such problems pose an obstacle for scalability since traditional global
optimizers are often too inefficient and slow for even small-scale processors
comprising tens of qubits. In this whitepaper, we introduce the Snake Optimizer
for efficiently and quickly solving such optimization problems by leveraging
concepts in artificial intelligence, dynamic programming, and graph
optimization. In practice, the Snake has been applied to optimize the
frequencies at which quantum logic gates are implemented in frequency-tunable
superconducting qubits. This application enabled state-of-the-art system
performance on a 53 qubit quantum processor, serving as a key component of
demonstrating quantum supremacy. Furthermore, the Snake Optimizer scales
favorably with qubit number and is amenable to both local re-optimization and
parallelization, showing promise for optimizing much larger quantum processors.
