Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
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| Publicado en: | arXiv.org (Jun 5, 2019), p. n/a |
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| Autor principal: | |
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| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2159019415 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1103/PhysRevA.100.062334 |2 doi | |
| 035 | |a 2159019415 | ||
| 045 | 0 | |b d20190605 | |
| 100 | 1 | |a Wang, Wenyuan | |
| 245 | 1 | |a Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution | |
| 260 | |b Cornell University Library, arXiv.org |c Jun 5, 2019 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a For a practical quantum key distribution (QKD) system, parameter optimization - the choice of intensities and probabilities of sending them - is a crucial step in gaining optimal performance, especially when one realistically considers finite communication time. With the increasing interest in the field to implement QKD over free-space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the Internet of Things (IoT), a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search, or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a new method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board-computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 watts, and we find a speedup of up to 100-1000 times when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve over 95-99% of the optimal secure key rate. Moreover, our approach is highly general and not limited to any specific QKD protocol. | |
| 653 | |a Parallel processing | ||
| 653 | |a Background noise | ||
| 653 | |a Quantum cryptography | ||
| 653 | |a Misalignment | ||
| 653 | |a Tables | ||
| 653 | |a Neural networks | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Convexity | ||
| 653 | |a Electronic devices | ||
| 653 | |a Optimization | ||
| 653 | |a Search algorithms | ||
| 653 | |a Machine learning | ||
| 653 | |a Size effects | ||
| 653 | |a Personal computers | ||
| 653 | |a Predictions | ||
| 653 | |a Parameters | ||
| 653 | |a Computational geometry | ||
| 653 | |a Computing time | ||
| 700 | 1 | |a Hoi-Kwong Lo | |
| 773 | 0 | |t arXiv.org |g (Jun 5, 2019), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2159019415/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1812.07724 |