×

Cryptosystem for grid data based on quantum convolutional neural networks and quantum chaotic map. (English) Zbl 1523.81058

Summary: Motivated by the existing circuit model of quantum convolutional neural network, a new quantum convolutional neural network circuit model is devised, which is combined with quantum chaotic map to construct a symmetric cryptosystem. Quantum chaotic map produces key stream for encryption and decryption. The cryptosystem simulates the basic process of communication. Theoretical analysis manifests that the cryptosystem is effective. Additionally, simulation experiments based on MNIST data set show that the cryptosystem is secure. Furthermore, the proposed cryptosystem can be applied not only for image data, but for text data. Therefore, the grid data can be encrypted by utilizing the cryptosystem.

MSC:

81P94 Quantum cryptography (quantum-theoretic aspects)
81P45 Quantum information, communication, networks (quantum-theoretic aspects)
Full Text: DOI

References:

[1] Zhou, N.R., Zhu, K.N., Zou, X.F.: Multi-party semi-quantum key distribution protocol with four-particle cluster states. Annalen Der Physik 531(8) (2019) · Zbl 07761444
[2] Fei, G.; Qiaoyan, W.; Fuchen, Z., Teleportation attack on the QSDC protocol with a random basis and order, Chinese Physics B, 17, 9, 3189-3193 (2008) · doi:10.1088/1674-1056/17/9/006
[3] Zhou, NR; Hua, TX; Gong, LH; Pei, DJ; Liao, QH, Quantum image encryption based on generalized Arnold transform and double random-phase encoding, Quantum Inf. Process, 14, 4, 1193-1213 (2015) · Zbl 1328.81097 · doi:10.1007/s11128-015-0926-z
[4] Wang, J.; Geng, Y.; Han, L.; Liu, J., Quantum image encryption algorithm based on quantum key image, Int. J. Theor. Phys., 58, 1, 308-322 (2019) · Zbl 1412.81107 · doi:10.1007/s10773-018-3932-y
[5] Miyake, S.; Nakamae, K., A quantum watermarking scheme using simple and small-scale quantum circuits, Quantum Inf. Process, 15, 5, 1849-1864 (2016) · Zbl 1338.81141 · doi:10.1007/s11128-016-1260-9
[6] Cao, Y., Guerreschi, G.G., Aspuruguzik, A.: Quantum neuron: an elementary building block for machine learning on quantum computers. arxiv: Quantum Physics (2017)
[7] Chatterjee, R.; Yu, T., Generalized coherent states, reproducing kernels, and quantum support vector machines, Quantum Information & Computation, 17, 15, 1292-1306 (2017) · doi:10.26421/QIC17.15-16-3
[8] Shor, PW; Preskill, J., Simple proof of security of the BB84 quantum key distribution protocol, Phys. Rev. Lett., 85, 2, 441-444 (2000) · doi:10.1103/PhysRevLett.85.441
[9] Mao, C., Li, J., Zhu, J., Zhang, C., Wang, Q.: An improved proposal on the practical quantum key distribution with biased basis. Quantum Information Processing 16(10) (2017) · Zbl 1387.81191
[10] Lai, H.; Luo, MX; Zhan, C.; Pieprzyk, J.; Orgun, MA, An improved coding method of quantum key distribution protocols based on Fibonacci-valued OAM entangled states, Phys. Lett. A, 381, 35, 2922-2926 (2017) · Zbl 1404.81081 · doi:10.1016/j.physleta.2017.07.015
[11] Yan, X.Y., Zhou, N.R., Gong, L.H., Wang, Y.Q., Wen, X.J.: High-dimensional quantum key distribution based on qudits transmission with quantum Fourier transform. Quantum Information Processing 18(9) (2019) · Zbl 1504.81084
[12] Xu, QD; Chen, HY; Gong, LH; Zhou, NR, Quantum private comparison protocol based on four-particle GHZ states, Int. J. Theor. Phys., 59, 6, 1798-1806 (2020) · Zbl 1441.81081 · doi:10.1007/s10773-020-04446-9
[13] Zhang, S.; Chen, ZK; Shi, RH; Liang, FY, A novel quantum identity authentication based on Bell states, Int. J. Theor. Phys., 59, 1, 236-249 (2019) · Zbl 1433.81072 · doi:10.1007/s10773-019-04319-w
[14] Xu, LC; Chen, HY; Gong, LH; Zhou, NR, Multi-party semi-quantum secure direct communication protocol with cluster states, Int. J. Theor. Phys., 59, 7, 2175-2186 (2020) · Zbl 1447.81056 · doi:10.1007/s10773-020-04491-4
[15] Zhou, RG; Wu, Q.; Zhang, MQ; Shen, CY, Quantum image encryption and decryption algorithms based on quantum image geometric transformations, Int. J. Theor. Phys., 52, 6, 1802-1817 (2013) · doi:10.1007/s10773-012-1274-8
[16] Hua, TX; Chen, JM; Pei, DJ; Zhang, WQ; Zhou, NR, Quantum image encryption algorithm based on image correlation decomposition, International Journal of Theoretical Physics, 54, 2, 526-537 (2015) · Zbl 1312.81045 · doi:10.1007/s10773-014-2245-z
[17] Zhou, NR; Hu, YQ; Gong, LH; Li, GY, Quantum image encryption scheme with iterative generalized Arnold transforms and quantum image cycle shift operations, Quantum Inf. Process, 16, 6, 1-23 (2017) · Zbl 1373.81194
[18] Wang, J.; Geng, YC; Han, L.; Liu, JQ, Quantum image encryption algorithm based on quantum key image, Int. J. Theor. Phys., 58, 1, 308-322 (2019) · Zbl 1412.81107 · doi:10.1007/s10773-018-3932-y
[19] Li, H.S., Li, C.Y., Chen, X., Xia, H.Y.: Quantum image encryption based on phase-shift transform and quantum Haar wavelet packet transform. Modern Physics Letters A 34(26) (2019) · Zbl 1421.81030
[20] Wang, HQ; Song, XH; Chen, LL; Xie, W., A secret sharing scheme for quantum gray and color images based on encryption, Int. J. Theor. Phys., 58, 5, 1626-1650 (2019) · Zbl 1422.81093 · doi:10.1007/s10773-019-04057-z
[21] Qiu, JF; Wu, QH; Ding, GR; Xu, YH; Feng, S., A survey of machine learning for big data processing, EURASIP Journal on Advances in Signal Processing., 2016, 67 (2016) · doi:10.1186/s13634-016-0355-x
[22] Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Physical Review Letters 113(13) (2014)
[23] Amin, M., Andriyash, E., Rolfe, J.T., Kulchytskyy, B., Melko, R.G.: Quantum boltzmann machine. Physical Review X 8(2) (2018)
[24] Schuld, M., Killoran, N.: Quantum machine learning in feature Hilbert spaces. Physical Review Letters 122(4) (2019)
[25] Lloyd, S.; Garnerone, S.; Zanardi, P., Quantum algorithms for topological and geometric analysis of data, Nat. Commun., 7, 1, 10138-10138 (2016) · doi:10.1038/ncomms10138
[26] Bishwas, AK; Mani, A.; Palade, V., An all-pair quantum SVM approach for big data multiclass classification, Quantum Inf. Process, 17, 10, 282 (2018) · Zbl 1400.68167 · doi:10.1007/s11128-018-2046-z
[27] Li, YY; Xiao, JJ; Chen, YQ; Jao, LC, Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification, Neurocomputing, 362, 156-165 (2019) · doi:10.1016/j.neucom.2019.07.026
[28] Youssry, A.; Rafei, A.; Elramly, S., A quantum mechanics-based framework for image processing and its application to image segmentation, Quantum Inf. Process, 14, 10, 3613-3638 (2015) · Zbl 1327.81145 · doi:10.1007/s11128-015-1072-3
[29] Dallairedemers, P., Killoran, N.: Quantum generative adversarial networks. Physical Review A 98(1) (2018)
[30] Wang, YX; Wang, RJ; Li, DF; Adu, D.; Tian, KB; Zhu, YX, Improved handwritten digit recognition using quantum K-Nearest-Neighbor algorithm, Int. J. Theor. Phys., 58, 7, 2331-2340 (2019) · Zbl 1433.81059 · doi:10.1007/s10773-019-04124-5
[31] Wan, KH; Dahlsten, O.; Kristjánsson, H.; Gardner, R.; Kim, MS, Quantum generalisation of feedforward neural networks, NPJ Quantum Information, 3, 36 (2017) · doi:10.1038/s41534-017-0032-4
[32] Shi, J.; Chen, S.; Lu, Y.; Feng, Y.; Shi, R.; Yang, Y.; Li, J., An approach to cryptography based on continuous-variable quantum neural network, Sci. Rep., 10, 2107 (2020) · doi:10.1038/s41598-020-58928-1
[33] Gao, X.; Duan, LM, Efficient representation of quantum many-body states with deep neural networks, Nat. Commun., 8, 2041-1723 (2017) · doi:10.1038/s41467-017-01952-z
[34] Cong, I.; Choi, S.; Lukin, MD, Quantum convolutional neural networks, Nat. Phys., 15, 12, 1273-1278 (2019) · doi:10.1038/s41567-019-0648-8
[35] Henderson, M., Shakya, S., Pradhan, S., Cook, T.: Quanvolutional neural networks: powering image recognition with quantum circuits. arxiv: Quantum Physics (2019)
[36] Pareek, NK; Patidar, V.; Sud, KK, Image encryption using chaotic logistic map, Image Vis. Comput., 24, 9, 926-934 (2016) · doi:10.1016/j.imavis.2006.02.021
[37] Lu, X.; Jiang, N.; Hu, H.; Ji, Z., Quantum adder for superposition states, Int. J. Theor. Phys., 57, 9, 2575-2584 (2018) · Zbl 1412.81102 · doi:10.1007/s10773-018-3779-2
[38] Kotiyal, S., Thapliyal, H., Ranganathan, N.: Circuit for reversible quantum multiplier based on binary tree optimizing ancilla and garbage bits. In: 2014 27th International Conference on VLSI Design and 2014 13th International Conference on Embedded Systems, pp 545-550. IEEE (2014)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.