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Quantum Information Processing

Optimal resource allocation of quantum digital signatures with machine learning

  • 01-09-2022
Published in:
Authors
Jia-Xin Xu
Zi-Ang Ren
Yi-Peng Chen
Chun-Hui Zhang
Qin Wang
Published in
Quantum Information Processing | Issue 9/2022

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Abstract

The article delves into the optimization of quantum digital signatures (QDS) using machine learning, addressing the challenges posed by traditional methods. It highlights the development of QDS protocols, from early experiments to the integration of machine learning for real-time optimization. The random forest algorithm is employed to predict optimal protocols and system parameters, showcasing its superior performance in terms of accuracy and efficiency compared to traditional methods. The article underscores the potential of machine learning to pave the way for practical applications of large-scale QDS networks, making it a compelling read for specialists in quantum computing and data science.
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previous article Quadratic and higher-order unconstrained binary optimization of railway rescheduling for quantum computing
next article Quantifying coherence of quantum channels via trace distance
Title
Optimal resource allocation of quantum digital signatures with machine learning
Authors
Jia-Xin Xu
Zi-Ang Ren
Yi-Peng Chen
Chun-Hui Zhang
Qin Wang
Publication date
01-09-2022
Publisher
Springer US
Published in
Quantum Information Processing / Issue 9/2022
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-022-03672-w
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Detection of genuine entanglement for multipartite quantum states

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Quantifying coherence of quantum channels via trace distance

Quantum reversible circuits for audio watermarking based on echo hiding technique

Improved semi-quantum key distribution with two almost-classical users