Prediction and real-time compensation of qubit decoherence via machine learning
Control engineering techniques are promising for realizing stable quantum systems to counter their extreme fragility. Here the authors use techniques from machine learning to enable real-time feedback suppression of decoherence in a trapped ion qubit by predicting its future stochastic evolution.
Main Authors: | Sandeep Mavadia, Virginia Frey, Jarrah Sastrawan, Stephen Dona, Michael J. Biercuk |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2017-01-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/ncomms14106 |
Similar Items
-
Arbitrary quantum control of qubits in the presence of universal noise
by: Todd J Green, et al.
Published: (2013-01-01) -
Qubit decoherence and symmetry restoration through real-time instantons
by: Foster Thompson, et al.
Published: (2022-04-01) -
Real-time adaptive estimation of decoherence timescales for a single qubit
by: Arshad, MJ, et al.
Published: (2024) -
Phonon-induced decoherence in color-center qubits
by: Prajit Dhara, et al.
Published: (2024-01-01) -
Anisotropy with respect to the applied magnetic field of spin qubit decoherence times
by: Yujun Choi, et al.
Published: (2022-06-01)