Estimating unknown qubit phase under telegraph noises using recurrent neural network
Issued Date
2023-01-01
Resource Type
ISSN
17426588
eISSN
17426596
Scopus ID
2-s2.0-85148034963
Journal Title
Journal of Physics: Conference Series
Volume
2431
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Physics: Conference Series Vol.2431 No.1 (2023)
Suggested Citation
Rojanasirivanit S., Pathumsoot P., Chantasri A. Estimating unknown qubit phase under telegraph noises using recurrent neural network. Journal of Physics: Conference Series Vol.2431 No.1 (2023). doi:10.1088/1742-6596/2431/1/012103 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82248
Title
Estimating unknown qubit phase under telegraph noises using recurrent neural network
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
We consider a system of qubits affected by an environmental random-telegraph process (RTP) noise and investigate the recently proposed idea of using a spectator qubit (SQ) as a noise probe to remove noise effects from a data qubit (DQ). By measuring the SQ at various times with particular measurement bases, their measurement readouts can be used to extract the information of the noise effects. In this work, we propose the use of recurrent neural network (RNN), with the Long short-term memory (LSTM) layers, to process the SQ's readouts and train the model to estimate the errors occurred in the DQ. We present numerical results of the DQ's and SQ's phases and show that the LSTM can estimate the unknown DQ's phase with the estimation accuracy depending on the SQ's noise sensitivity.