Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes

Temitope Bolaji Adeniyi; Sathish Kumar

doi:10.1109/CCAAW57883.2023.10219406

Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes

Temitope Bolaji Adeniyi
, Sathish Kumar

Computer Science

Cleveland State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Quantum gyroscopes (QG) are a critical component of navigation systems in space applications. However, these devices are prone to noise and environmental factors that can degrade the quality of the data and compromise the accuracy of the measurements. This research proposes an effective Adversarial Autoencoders (AAEs) framework for denoising and signal recovery in QGs to improve the accuracy and reliability of navigation systems in space. By denoising signals, AAEs can improve the accuracy of measurements and can help reduce the risk of communication failures and increase the efficiency of cognitive communication systems. Overall, our method offers a promising solution for denoising and signal recovery in noisy QG schemes, demonstrating that it can accurately recover signals with low reconstruction error. This approach has the potential to improve the reliability and accuracy of QGs, leading to safer and more efficient communication systems.

Original language	English
Title of host publication	2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350335675
DOIs	https://doi.org/10.1109/CCAAW57883.2023.10219406
State	Published - Jan 1 2023
Event	2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023 - Cleveland, United States Duration: Jun 20 2023 → Jun 22 2023

Conference

Conference	2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023
Country/Territory	United States
City	Cleveland
Period	06/20/23 → 06/22/23

Keywords

Deep learning
Neural network
Quantum Metrology
Quantum Sensing
Quantum Sensors

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10.1109/CCAAW57883.2023.10219406

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title = "Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes",

abstract = "Quantum gyroscopes (QG) are a critical component of navigation systems in space applications. However, these devices are prone to noise and environmental factors that can degrade the quality of the data and compromise the accuracy of the measurements. This research proposes an effective Adversarial Autoencoders (AAEs) framework for denoising and signal recovery in QGs to improve the accuracy and reliability of navigation systems in space. By denoising signals, AAEs can improve the accuracy of measurements and can help reduce the risk of communication failures and increase the efficiency of cognitive communication systems. Overall, our method offers a promising solution for denoising and signal recovery in noisy QG schemes, demonstrating that it can accurately recover signals with low reconstruction error. This approach has the potential to improve the reliability and accuracy of QGs, leading to safer and more efficient communication systems.",

keywords = "Deep learning, Neural network, Quantum Metrology, Quantum Sensing, Quantum Sensors",

author = "Adeniyi, \{Temitope Bolaji\} and Sathish Kumar",

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doi = "10.1109/CCAAW57883.2023.10219406",

language = "English",

booktitle = "2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023 ; Conference date: 20-06-2023 Through 22-06-2023",

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Adeniyi, TB & Kumar, S 2023, Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes. in 2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023. Institute of Electrical and Electronics Engineers Inc., usa, 2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023, Cleveland, United States, 06/20/23. https://doi.org/10.1109/CCAAW57883.2023.10219406

Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes. / Adeniyi, Temitope Bolaji; Kumar, Sathish.
2023 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2023. usa: Institute of Electrical and Electronics Engineers Inc., 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes

AU - Adeniyi, Temitope Bolaji

AU - Kumar, Sathish

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Quantum gyroscopes (QG) are a critical component of navigation systems in space applications. However, these devices are prone to noise and environmental factors that can degrade the quality of the data and compromise the accuracy of the measurements. This research proposes an effective Adversarial Autoencoders (AAEs) framework for denoising and signal recovery in QGs to improve the accuracy and reliability of navigation systems in space. By denoising signals, AAEs can improve the accuracy of measurements and can help reduce the risk of communication failures and increase the efficiency of cognitive communication systems. Overall, our method offers a promising solution for denoising and signal recovery in noisy QG schemes, demonstrating that it can accurately recover signals with low reconstruction error. This approach has the potential to improve the reliability and accuracy of QGs, leading to safer and more efficient communication systems.

AB - Quantum gyroscopes (QG) are a critical component of navigation systems in space applications. However, these devices are prone to noise and environmental factors that can degrade the quality of the data and compromise the accuracy of the measurements. This research proposes an effective Adversarial Autoencoders (AAEs) framework for denoising and signal recovery in QGs to improve the accuracy and reliability of navigation systems in space. By denoising signals, AAEs can improve the accuracy of measurements and can help reduce the risk of communication failures and increase the efficiency of cognitive communication systems. Overall, our method offers a promising solution for denoising and signal recovery in noisy QG schemes, demonstrating that it can accurately recover signals with low reconstruction error. This approach has the potential to improve the reliability and accuracy of QGs, leading to safer and more efficient communication systems.

KW - Deep learning

KW - Neural network

KW - Quantum Metrology

KW - Quantum Sensing

KW - Quantum Sensors

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M3 - Conference contribution

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ER -

Adversarial Autoencoder for Denoising and Signal Recovery in Quantum Gyroscopes

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Keywords

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