The Snake Optimizer for Learning Quantum Processor Control Parameters

TL;DR

The paper introduces the Snake Optimizer, a novel AI-driven method for rapidly calibrating quantum processors by solving complex, high-dimensional optimization problems, demonstrated on a 53-qubit system to enhance quantum computing performance.

Contribution

The Snake Optimizer is a new algorithm that efficiently addresses non-convex, high-dimensional quantum control problems, improving scalability and speed over traditional methods.

Findings

Enabled state-of-the-art performance on a 53-qubit processor

Scales favorably with increasing qubit number

Supports local re-optimization and parallelization

Abstract

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.