Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination

Peng Duan, Zi-Feng Chen, Qi Zhou, Wei-Cheng Kong, Hai-Feng Zhang, and Guo-Ping Guo
Phys. Rev. Applied 16, 024063 – Published 31 August 2021

Abstract

Measurement of qubits plays a key role in quantum computation. In superconducting multiqubit quantum processors, a multiplexed readout scheme is widely used. In such a scheme, measurement of a qubit state may be influenced by the state of neighboring qubits, due to various crosstalk effects, which will degrade the readout fidelity. To reduce the impact of crosstalk, we model the digital signal processing system used in measurements as a shallow neural network and train it to become a state discriminator. Applying our method to a six-qubit superconducting quantum chip, we see an overall improved readout performance compared with a contemporary qubit-state discriminator. The readout crosstalk is decreased by more than 80%. The training and optimization process of the neural network consumes only about 10 s.

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  • Received 19 March 2021
  • Revised 2 August 2021
  • Accepted 13 August 2021

DOI:https://doi.org/10.1103/PhysRevApplied.16.024063

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Peng Duan1,2,†, Zi-Feng Chen1,2,†, Qi Zhou1,2, Wei-Cheng Kong3, Hai-Feng Zhang1,2, and Guo-Ping Guo1,2,3,*

  • 1CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 2CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 3Origin Quantum Computing Company Limited, Hefei, Anhui 230026, China

  • *gpguo@ustc.edu.cn
  • These authors contributed equally to this work.

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Vol. 16, Iss. 2 — August 2021

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