Quantum Error Mitigation With Artificial Neural Network

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 32
  • Download : 7
A quantum error mitigation technique based on machine learning is proposed, which learns how to adjust the probabilities estimated by measurement in the computational basis. Neural networks in two different designs are trained with random quantum circuits consisting of a set of one- and two-qubit unitary gates whose measurement statistics in the ideal (noiseless) and real (noisy) cases are known. Once the neural networks are trained, they infer the amount of probability adjustment to be made on the measurement obtained from executing an unseen quantum circuit to reduce the error. The proposed schemes are tested with two-, three-, five-, and seven-qubit quantum circuits of depth up to 20 by computer simulations with realistic error models and experiments using the IBM quantum cloud platform. In all test cases, the proposed mitigation technique reduces the error effectively. Our method can be used to improve the accuracy of noisy intermediate-scale quantum (NISQ) algorithms without relying on extensive error characterization or quantum error correction.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-10
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.188853 - 188860

ISSN
2169-3536
DOI
10.1109/ACCESS.2020.3031607
URI
http://hdl.handle.net/10203/278677
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
000584721600001.pdf(947.62 kB)Download

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0