Iterative supervised learning of quantum processor error models

Abstract

Systems and methods for generating error models for quantum algorithms implemented on quantum processors having a plurality of qubits are provided. In one example, a method includes obtaining data associated with a benchmark model, the benchmark model having one or more error indicators as features, one or more benchmarks as targets, and one or more trainable parameters, wherein each error indicator is associated with a distinct quantum gate calibrated in a distinct operating configuration associated with a plurality of operating parameters for the quantum gate and associated with a calibration data for the operating configuration. The method includes determining parameter values for the trainable parameters. The method include operating a quantum computing system based on operating parameters determined based on the parameter values.

CPC Classifications