The Snake Optimizer for Learning Quantum Processor Control Parameters

Understand

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.