D50: Holistic QCVV Techniques and Shadow Tomography

Mon. March 4, 3:00 p.m. – 3:12 p.m. CST

200H

Predictive models of quantum computing devices are essential for understanding quantum devices' behavior, quantifying the rates and kinds of errors, and enabling engineering improvements. But models for a processor's behavior—whether derived from theory or from experimental characterization protocols run on a device—are not always accurate. Models are useful only inasmuch as their predictions are accurate, and inconsistencies with experimental data suggest that a model should be adjusted or replaced. We present simple and easy-to-use techniques for testing the validity of models using experimental data from running quantum circuits, and we demonstrate them using data from cloud-access quantum computers. We begin by showing how to determine if a model is consistent with a data set. Most real world models are not fully consistent with data, so we then show how to probe the inconsistencies between the model and the data by quantifying and analyzing the deviations using operationally meaningful error metrics.