A physics-based machine learning approach to quantum device characterization

Modern quantum computers are noisy and error-prone. They are also growing rapidly, necessitating the development of scalable methods for predicting their performance. Although unobservable, estimations of process fidelity provide one path to scalable benchmarks. In this work, we use existing scalable methods for estimating process fidelity to train neural networks to predict the process fidelity of unknown circuits of interest. As off-the-shelf neural networks are agnostic to the underlying physics present in a quantum device, we augment their training with information from traditional physics-based modelling approaches, ultimately allowing these neural networks to serve as device proxies for quick and accurate querying and estimation of a device’s performance.

This work was supported by the LDRD program at Sandia National Labs. Sandia National Labs is a multimission laboratory managed and operated by NTESS, LLC, a wholly owned subsidiary of Honeywell International Inc., for DOE’s NNSA under contract DE-NA0003525.