Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence
Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/26/3/207 |
| _version_ | 1797241207006429184 |
|---|---|
| author | Jianmin Yi Hao Wu Ying Guo |
| author_facet | Jianmin Yi Hao Wu Ying Guo |
| author_sort | Jianmin Yi |
| collection | DOAJ |
| description | Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation. |
| first_indexed | 2024-04-24T18:19:39Z |
| format | Article |
| id | doaj.art-ea2edba869cf4a718dcf366074de1932 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-24T18:19:39Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-ea2edba869cf4a718dcf366074de19322024-03-27T13:36:50ZengMDPI AGEntropy1099-43002024-02-0126320710.3390/e26030207Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic TurbulenceJianmin Yi0Hao Wu1Ying Guo2School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaBuilding an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation.https://www.mdpi.com/1099-4300/26/3/207continuous variable quantum key distributionmeasurement-device-independentoceanic turbulence modelneural network |
| spellingShingle | Jianmin Yi Hao Wu Ying Guo Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence Entropy continuous variable quantum key distribution measurement-device-independent oceanic turbulence model neural network |
| title | Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence |
| title_full | Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence |
| title_fullStr | Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence |
| title_full_unstemmed | Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence |
| title_short | Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence |
| title_sort | passive continuous variable measurement device independent quantum key distribution predictable with machine learning in oceanic turbulence |
| topic | continuous variable quantum key distribution measurement-device-independent oceanic turbulence model neural network |
| url | https://www.mdpi.com/1099-4300/26/3/207 |
| work_keys_str_mv | AT jianminyi passivecontinuousvariablemeasurementdeviceindependentquantumkeydistributionpredictablewithmachinelearninginoceanicturbulence AT haowu passivecontinuousvariablemeasurementdeviceindependentquantumkeydistributionpredictablewithmachinelearninginoceanicturbulence AT yingguo passivecontinuousvariablemeasurementdeviceindependentquantumkeydistributionpredictablewithmachinelearninginoceanicturbulence |