Quantum readout error mitigation via deep learning

Jihye Kim, Byungdu Oh, Yonuk Chong, Euyheon Hwang, Daniel K. Park

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices.

Original languageEnglish
Article number073009
JournalNew Journal of Physics
Issue number7
Publication statusPublished - 2022 Jul 1

Bibliographical note

Funding Information:
This research is supported by the National Research Foundation of Korea (Grant No. 2019R1I1A1A01050161, No. 2021M3H3A1038085 and No. 2022M3E4A1074591). We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team.

Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)


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