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Low-dimensional reconciliation for continuous-variable quantum key distribution

Low-dimensional reconciliation for continuous-variable quantum key distribution
Low-dimensional reconciliation for continuous-variable quantum key distribution

We propose an efficient logical layer-based reconciliation method for continuous-variable quantum key distribution (CVQKD) to extract binary information from correlated Gaussian variables. We demonstrate that by operating on the raw-data level, the noise of the quantum channel can be corrected in the low-dimensional (scalar) space, and the reconciliation can be extended to arbitrary dimensions. The CVQKD systems allow an unconditionally secret communication over standard telecommunication networks. To exploit the real potential of CVQKD a robust reconciliation technique is needed. It is currently unavailable, which makes it impossible to reach the real performance of the CVQKD protocols. The reconciliation is a post-processing step separated from the transmission of quantum states, which is aimed to derive the secret key from the raw data. The reconciliation process of correlated Gaussian variables is a complex problem that requires either tomography in the physical layer that is intractable in a practical scenario, or high-cost calculations in the multidimensional spherical space with strict dimensional limitations. To avoid these issues, we define the low-dimensional reconciliation. We prove that the error probability of one-dimensional reconciliation is zero in any practical CVQKD scenario, and provides unconditional security. The results allow for significantly improving the currently available key rates and transmission distances of CVQKD.

Continuous-variable quantum key distribution, Quantum Shannon theory
2076-3417
Gyongyosi, Laszlo
bbfffd90-dfa2-4a08-b5f9-98376b8d6803
Imre, Sandor
2def242c-1cb7-4b12-8a16-351a5a36e041
Gyongyosi, Laszlo
bbfffd90-dfa2-4a08-b5f9-98376b8d6803
Imre, Sandor
2def242c-1cb7-4b12-8a16-351a5a36e041

Gyongyosi, Laszlo and Imre, Sandor (2018) Low-dimensional reconciliation for continuous-variable quantum key distribution. Applied Sciences, 8 (1). (doi:10.3390/app8010087).

Record type: Article

Abstract

We propose an efficient logical layer-based reconciliation method for continuous-variable quantum key distribution (CVQKD) to extract binary information from correlated Gaussian variables. We demonstrate that by operating on the raw-data level, the noise of the quantum channel can be corrected in the low-dimensional (scalar) space, and the reconciliation can be extended to arbitrary dimensions. The CVQKD systems allow an unconditionally secret communication over standard telecommunication networks. To exploit the real potential of CVQKD a robust reconciliation technique is needed. It is currently unavailable, which makes it impossible to reach the real performance of the CVQKD protocols. The reconciliation is a post-processing step separated from the transmission of quantum states, which is aimed to derive the secret key from the raw data. The reconciliation process of correlated Gaussian variables is a complex problem that requires either tomography in the physical layer that is intractable in a practical scenario, or high-cost calculations in the multidimensional spherical space with strict dimensional limitations. To avoid these issues, we define the low-dimensional reconciliation. We prove that the error probability of one-dimensional reconciliation is zero in any practical CVQKD scenario, and provides unconditional security. The results allow for significantly improving the currently available key rates and transmission distances of CVQKD.

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More information

Accepted/In Press date: 5 January 2018
e-pub ahead of print date: 9 January 2018
Published date: 9 January 2018
Keywords: Continuous-variable quantum key distribution, Quantum Shannon theory

Identifiers

Local EPrints ID: 417425
URI: https://eprints.soton.ac.uk/id/eprint/417425
ISSN: 2076-3417
PURE UUID: 0103f286-5610-4fdf-bc46-d44b9ac18bd6

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Date deposited: 31 Jan 2018 17:30
Last modified: 31 Jan 2018 17:30

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Contributors

Author: Laszlo Gyongyosi
Author: Sandor Imre

University divisions

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