Predictive Models from Quantum Computer Benchmarks
Daniel Hothem Jordan Hines Karthik Nataraj Robin Blume-Kohout Timothy Proctor
Holistic benchmarks for quantum computers are essential for testing and
summarizing the performance of quantum hardware. However, holistic benchmarks
-- such as algorithmic or randomized benchmarks -- typically do not predict a
processor's performance on circuits outside the benchmark's necessarily very
limited set of test circuits. In this paper, we introduce a general framework
for building predictive models from benchmarking data using capability models.
Capability models can be fit to many kinds of benchmarking data and used for a
variety of predictive tasks. We demonstrate this flexibility with two case
studies. In the first case study, we predict circuit (i) process fidelities and
(ii) success probabilities by fitting error rates models to two kinds of
volumetric benchmarking data. Error rates models are simple, yet versatile
capability models which assign effective error rates to individual gates, or
more general circuit components. In the second case study, we construct a
capability model for predicting circuit success probabilities by applying
transfer learning to ResNet50, a neural network trained for image
classification. Our case studies use data from cloud-accessible quantum
computers and simulations of noisy quantum computers.