Skip to main content
Journal cover image

Machine learning assisted readout of trapped-ion qubits

Publication ,  Journal Article
Seif, A; Landsman, KA; Linke, NM; Figgatt, C; Monroe, C; Hafezi, M
Published in: Journal of Physics B: Atomic, Molecular and Optical Physics
August 17, 2018

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Journal of Physics B: Atomic, Molecular and Optical Physics

DOI

EISSN

1361-6455

ISSN

0953-4075

Publication Date

August 17, 2018

Volume

51

Issue

17

Related Subject Headings

  • General Physics
  • 5108 Quantum physics
  • 5106 Nuclear and plasma physics
  • 5102 Atomic, molecular and optical physics
  • 0307 Theoretical and Computational Chemistry
  • 0205 Optical Physics
  • 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Seif, A., Landsman, K. A., Linke, N. M., Figgatt, C., Monroe, C., & Hafezi, M. (2018). Machine learning assisted readout of trapped-ion qubits. Journal of Physics B: Atomic, Molecular and Optical Physics, 51(17). https://doi.org/10.1088/1361-6455/aad62b
Seif, A., K. A. Landsman, N. M. Linke, C. Figgatt, C. Monroe, and M. Hafezi. “Machine learning assisted readout of trapped-ion qubits.” Journal of Physics B: Atomic, Molecular and Optical Physics 51, no. 17 (August 17, 2018). https://doi.org/10.1088/1361-6455/aad62b.
Seif A, Landsman KA, Linke NM, Figgatt C, Monroe C, Hafezi M. Machine learning assisted readout of trapped-ion qubits. Journal of Physics B: Atomic, Molecular and Optical Physics. 2018 Aug 17;51(17).
Seif, A., et al. “Machine learning assisted readout of trapped-ion qubits.” Journal of Physics B: Atomic, Molecular and Optical Physics, vol. 51, no. 17, Aug. 2018. Scopus, doi:10.1088/1361-6455/aad62b.
Seif A, Landsman KA, Linke NM, Figgatt C, Monroe C, Hafezi M. Machine learning assisted readout of trapped-ion qubits. Journal of Physics B: Atomic, Molecular and Optical Physics. 2018 Aug 17;51(17).
Journal cover image

Published In

Journal of Physics B: Atomic, Molecular and Optical Physics

DOI

EISSN

1361-6455

ISSN

0953-4075

Publication Date

August 17, 2018

Volume

51

Issue

17

Related Subject Headings

  • General Physics
  • 5108 Quantum physics
  • 5106 Nuclear and plasma physics
  • 5102 Atomic, molecular and optical physics
  • 0307 Theoretical and Computational Chemistry
  • 0205 Optical Physics
  • 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics