Simulation and Benchmarking of Real Quantum Hardware

TL;DR

This paper develops and evaluates a noise model for superconducting quantum hardware, enabling more accurate simulation and benchmarking of quantum algorithms on noisy intermediate-scale quantum devices.

Contribution

The paper introduces a new noise model tailored for superconducting hardware and demonstrates its improved accuracy in simulating a 20-qubit quantum computer.

Findings

The noise model accurately predicts hardware behavior.

Simulation results outperform existing models.

Benchmarking shows improved prediction accuracy.

Abstract

The effects of noise are one of the most important factors to consider when it comes to quantum computing in the noisy intermediate-scale quantum computing (NISQ) era that we are currently in. Therefore, it is important not only to gain more knowledge about the noise sources appearing in current quantum computing hardware in order to suppress and mitigate their contributions, but also to evaluate whether a given quantum algorithm can achieve reasonable results on a given hardware. To accomplish this, we need noise models that can describe the real hardware with sufficient accuracy. Here, we present a noise model that has been evaluated on superconducting hardware platforms and could be adapted to other common architectures such as trapped-ion or neutral atom devices. We then benchmark our model by simulating a 20-qubit superconducting quantum computer, and compare the accuracy of our model to similar approaches from the literature and demonstrate an improvement in the overall prediction accuracy.