A methodology to select and adjust quantum noise models through emulators: benchmarking against real backends

Abstract

Currently, access to quantum processors is costly in terms of time, and power. There are quantum simulators and emulators on the market that offer alternatives for evaluating the behavior of a real quantum processor. However, these emulation environments present accuracy deviations from real devices, mainly because of difficult-to-model error sources. In this study, a methodology is proposed that allows the selection of noise models and adjustment of their parameters, considering the nature of the backends (technology, topology, vendor, model, etc.). The proposed methodology is illustrated using a small superconducting example based on the ibm_perth backend (seven qubits) and a comparison between the quantum emulators Qaptiva and Qiskit, where six different noise models are applied, achieving a fidelity deviation of 0.686% at best with respect to the real device.