Machine learning for continuous quantum error correction on superconducting qubits

Machine learning for continuous quantum error correction on superconducting qubits

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm fo...

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Bibliographic Details
Main Authors: Ian Convy, Haoran Liao, Song Zhang, Sahil Patel, William P Livingston, Ho Nam Nguyen, Irfan Siddiqi, K Birgitta Whaley
Format: Article
Language:English
Published: IOP Publishing 2022-01-01
Series:New Journal of Physics
Subjects:
quantum error correction
recurrent neural networks
Bayesian inference
quantum computing
superconducting qubits
machine learning
Online Access:https://doi.org/10.1088/1367-2630/ac66f9

Internet

https://doi.org/10.1088/1367-2630/ac66f9

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