Speaker
Description
Quantum computing is expected to offer numerous applications in science and industry. Its main obstacle is the erroneous behaviour of current devices. To counteract these errors with methods such as quantum error mitigation, understanding them and predicting their impacts on computations is essential. Thus, it is necessary to construct and evaluate accurate noise models. Moreover, the quality of such a noise model might depend on the kind of application and the quantum circuits used. Several papers deal with noise models for quantum computing, sometimes running a set of arbitrary experiments to compare model predictions with hardware data.
However, the evaluation of noise models does not follow a systematic approach, making it nearly impossible to estimate the models' accuracy for a given application. Without such an estimate, it is unclear which types of errors are most dominant and which mitigation methods can be expected to be most effective.
Therefore, we present a systematic approach to benchmark noise models for quantum computing. It involves defining and running representative quantum circuits of different depths and widths. The outcomes of the experiments are evaluated with meaningful success criteria to obtain an overall metric of a given noise model. This process is similar to volumetric benchmarking used to assess quantum hardware but transferred to comparing hardware results to model predictions instead of ideal behaviour. Furthermore, we perform such a benchmark for a noise model of our choice in the context of Variational Quantum Algorithms (VQAs).
