Volumetric Benchmarking of Error Mitigation with Qermit

Abstract

The detrimental effect of noise accumulates as quantum computers grow in size. In the case where devices are too small or noisy to perform error correction, error mitigation may be used. Error mitigation does not increase the fidelity of quantum states, but instead aims to reduce the approximation error in quantities of concern, such as expectation values of observables. However, it is as yet unclear which circuit types, and devices of which characteristics, benefit most from the use of error mitigation. Here we develop a methodology to assess the performance of quantum error mitigation techniques. Our benchmarks are volumetric in design, and are performed on different superconducting hardware devices. Extensive classical simulations are also used for comparison. We use these benchmarks to identify disconnects between the predicted and practical performance of error mitigation protocols, and to identify the situations in which their use is beneficial. To perform these experiments, and for the benefit of the wider community, we introduce Qermit - an open source python package for quantum error mitigation. Qermit supports a wide range of error mitigation methods, is easily extensible and has a modular graph-based software design that facilitates composition of error mitigation protocols and subroutines.

25 pages, Comments welcome; v2 - 26 pages, minor corrections, variance discussion in appendix, v3 - 28 pages, expanded discussion, experiments with varying noise in appendix

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Cited by in corpus (6)

• Quantum Error Mitigation
• Testing platform-independent quantum error mitigation on noisy quantum computers
• Mitigating Errors on Superconducting Quantum Processors through Fuzzy Clustering
• Tierkreis: A Dataflow Framework for Hybrid Quantum-Classical Computing
• Quantum error mitigation by layerwise Richardson extrapolation
• Improving the efficiency of learning-based error mitigation