Implementation of Machine Learning in Quantum Key Distributions

[1]  Zong-Wen Yu,et al.  Twin-field quantum key distribution with large misalignment error , 2018, Physical Review A.

[2]  Hoi-Kwong Lo,et al.  Machine learning for optimal parameter prediction in quantum key distribution , 2018, Physical Review A.

[3]  Won-Young Hwang Quantum key distribution with high loss: toward global secure communication. , 2003, Physical review letters.

[4]  Chun-Mei Zhang,et al.  280-km experimental demonstration of a quantum digital signature with one decoy state. , 2020, Optics letters.

[5]  Shuang Wang,et al.  Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network , 2018, Journal of the Optical Society of America B.

[6]  Qin Wang,et al.  Implementing full parameter optimization on decoy-state measurement-device-independent quantum key distributions under realistic experimental conditions , 2017 .

[7]  Qin Wang,et al.  Experimental three-state measurement-device-independent quantum key distribution with uncharacterized sources. , 2020, Optics letters.

[8]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[9]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[10]  Hui Liu,et al.  Measurement-Device-Independent Quantum Key Distribution Over a 404 km Optical Fiber. , 2016, Physical review letters.

[11]  Qin Wang,et al.  Predicting optimal parameters with random forest for quantum key distribution , 2020, Quantum Inf. Process..

[12]  Qiang Zhang,et al.  Experimental Twin-Field Quantum Key Distribution Through Sending-or-Not-Sending , 2019, Physical review letters.

[13]  Xiang‐Bin Wang,et al.  Beating the PNS attack in practical quantum cryptography , 2004 .

[14]  Sellami Ali,et al.  DECOY STATE QUANTUM KEY DISTRIBUTION , 2010 .

[15]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[16]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[17]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[18]  Qin Wang,et al.  Simulating of the measurement-device independent quantum key distribution with phase randomized general sources , 2014, Scientific Reports.

[19]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[20]  G. Guo,et al.  Approaching the ideal quantum key distribution with two-intensity decoy states , 2015 .

[21]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[22]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[23]  J. F. Dynes,et al.  Overcoming the rate–distance limit of quantum key distribution without quantum repeaters , 2018, Nature.

[24]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[25]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[26]  Hao Li,et al.  Sending-or-Not-Sending with Independent Lasers: Secure Twin-Field Quantum Key Distribution over 509 km. , 2020, Physical review letters.

[27]  Feihu Xu,et al.  Protocol choice and parameter optimization in decoy-state measurement-device-independent quantum key distribution , 2014, 1406.0188.

[28]  S. Guha,et al.  Fundamental rate-loss tradeoff for optical quantum key distribution , 2014, Nature Communications.

[29]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[30]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[31]  John Preskill,et al.  Security of quantum key distribution with imperfect devices , 2002, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[32]  Hoi-Kwong Lo,et al.  Practical Measurement Device Independent Quantum Key Distribution , 2013 .

[33]  Fabio Sciarrino,et al.  Machine Learning-Based Classification of Vector Vortex Beams. , 2020, Physical review letters.

[34]  M. Curty,et al.  Measurement-device-independent quantum key distribution. , 2011, Physical review letters.

[35]  Gilles Brassard,et al.  Quantum cryptography: Public key distribution and coin tossing , 2014, Theor. Comput. Sci..