1,147
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Design and analysis of quantum machine learning: a survey

, , , , , & show all
Article: 2312121 | Received 21 Aug 2023, Accepted 25 Jan 2024, Published online: 29 Mar 2024

References

  • Abel, S., Criado, J. C., & Spannowsky, M. (2022). Completely quantum neural networks. Physical Review A, 106(2), 022601. https://doi.org/10.1103/PhysRevA.106.022601
  • Abohashima, Z., Elhosen, M., Houssein, E. H., & Mohamed, W. M. (2020). Classification with quantum machine learning: A survey. arXiv preprint arXiv:2006. 12270, https://doi.org/10.48550/arXiv.2006.12270
  • Adcock, J. C., Allen, E. J., Day, M. L., Frick, S., Hinchliff, J. J., Johnson, M., Morley-Short, S., Pallister, S., Price, A. B., & Stanisic, S. (2015). Advances in quantum machine learning. arXiv: Quantum Physics, https://doi.org/10.48550/arXiv.1512.02900
  • Adhikary, S., Dangwal, S., & Bhowmik, D. (2020). Supervised learning with a quantum classifier using multi-level systems. Quantum Information Processing, 19(3), 1–12. https://doi.org/10.1007/s11128-020-2587-9
  • Allcock, J., & Hsieh, C. Y. (2020). A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time. Quantum, 4, 342. https://doi.org/10.22331/q-2020-10-15-342
  • Altaisky, M. V., Kaputkina, N. E., & Krylov, V. (2014). Quantum neural networks: Current status and prospects for development. Physics of Particles and Nuclei, 45(6), 1013–1032. https://doi.org/10.1134/S1063779614060033
  • Arrasmith, A., Holmes, Z., Cerezo, M., & Coles, P. J. (2022). Equivalence of quantum barren plateaus to cost concentration and narrow gorges. Quantum Science and Technology, 7(4), 045015. https://doi.org/10.1088/2058-9565/ac7d06
  • Arunachalam, S., Gheorghiu, V., Jochym-O'Connor, T., Mosca, M., & Srinivasan, P. (2015). On the robustness of bucket brigade quantum RAM. New Journal of Physics, 17(12), 123010. https://doi.org/10.1088/1367-2630/17/12/123010
  • Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. https://doi.org/10.1038/nature23474
  • Buffoni, L., & Caruso, F. (2021). New trends in quantum machine learning (a). Europhysics Letters, 132(6), 60004. https://doi.org/10.1209/0295-5075/132/60004
  • Bultrini, D., Wang, S., Czarnik, P., Gordon, M. H., Cerezo, M., Coles, P. J., & Cincio, L. (2023). The battle of clean and dirty qubits in the era of partial error correction. Quantum, 7, 1060. https://doi.org/10.22331/q-2023-07-13-1060
  • Cai, J., Liang, W., Li, X., Li, K. C., Gui, Z., & Khan, M. K. (2023). GTxchain: A deng, secure IoT smart blockchain architecture based on graph neural network. IEEE Internet of Things Journal, 10(24), 21502–21514.
  • Cao, B., Liu, J., Wen, Y., Li, H., Xiao, Q., & Chen, J. (2019). QoS-aware service recommendation based on relational topic model and factorization machines for IoT mashup applications. Journal of Parallel and Distributed Computing, 132, 177–189. https://doi.org/10.1016/j.jpdc.2018.04.002
  • Cao, B., Liu, X., Liu, J., & Tang, M. (2017). Domain-aware Mashup service clustering based on LDA topic model from multiple data sources. Information and Software Technology, 90, 40–54. https://doi.org/10.1016/j.infsof.2017.05.001
  • Cao, B., Liu, X., Rahman, M. M., Li, B., Liu, J., & Tang, M. (2020). Integrated content and network-based service clustering and web APIs recommendation for mashup development. IEEE Transactions on Services Computing, 13(1), 99–113. https://doi.org/10.1109/TSC.2017.2686390
  • Cerezo, M., Verdon, G., Huang, H. Y., Cincio, L., & Coles, P. J. (2022). Challenges and opportunities in quantum machine learning. Nature Computational Science, 2(9), 567–576. https://doi.org/10.1038/s43588-022-00311-3
  • Chen, H., Gao, Y., & Zhang, J. (2015). Quantum k-nearest neighbor algorithm. Dongnan Daxue Xuebao, 45(4), 647–651.
  • Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020). Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167
  • Chen, L., Shen, F., Tang, Y., Wang, X., & Wang, J. (2023). Algebraic structure based clustering method from granular computing prospective. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 31((01|1)), 121–140. https://doi.org/10.1142/S0218488523500083
  • Chen, L., Wang, J., & Li, L. (2016). The models of granular system and algebraic quotient space in granular computing. Chinese Journal of Electronics, 25(6), 1109–1113. https://doi.org/10.1049/cje.2016.08.001
  • Chen, L., Wang, J., Wang, W., & Li, L. (2019). A new granular computing model based on algebraic structure. Chinese Journal of Electronics, 28(1), 136–142. https://doi.org/10.1049/cje.2018.09.006
  • Chen, L., Zhao, L., Xiao, Z., Liu, Y., & Wang, J. Y. (2021). A granular computing based classification method from algebraic granule structure. IEEE Access, 9, 68118–68126. https://doi.org/10.1109/ACCESS.2021.3077409
  • Cirac, J. I., & Zoller, P. (1995). Quantum computations with cold trapped ions. Physical Review Letters, 74(20), 4091–4094. https://doi.org/10.1103/PhysRevLett.74.4091
  • Deng, Y., Hu, H., Xiong, N., Xiong, W., & Liu, L. (2015). A general hybrid model for chaos robust synchronization and degradation reduction. Information Sciences, 305, 146–164. https://doi.org/10.1016/j.ins.2015.01.028
  • Deutsch, D., & Jozsa, R. (1992). Rapid solution of problems by quantum computation. Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences, 439(1907), 553–558. https://doi.org/10.1098/rspa.1992.0167
  • Diao, C., Zhang, D., Liang, W., Li, K. C., Hong, Y., & Gaudiot, J. (2023). A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction. IEEE Transactions on Intelligent Transportation Systems, 24(1), 904–914. https://doi.org/10.1109/TITS.2022.3140229
  • Ding, C., Bao, T., & Huang, H. (2019). Quantum-inspired support vector machine. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 7210–7222. https://doi.org/10.1109/TNNLS.2021.3084467
  • Fakultat, M. (2006). Approaches to analyse and interpret biological profile data.
  • Feng, C., Zhao, B., Zhou, X., Ding, X., & Shan, Z. (2023). An enhanced quantum K-nearest neighbor classification algorithm based on polar distance. Entropy, 25(1), 127. https://doi.org/10.3390/e25010127
  • Feynman, R. P. (1999). Simulating physics with computers. International Journal of Theoretical Physics, 21(6–7), 467–488. https://doi.org/10.1007/BF02650179
  • Gao, L., Lu, C., Guo, G., Zhang, X., & Lin, S. (2022). Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance. Frontiers of Physics, 10, 1047466.
  • Garcia, D. P., Cruz-Benito, J., & Garc'ia-Penalvo, F. J. (2022). Systematic literature review: Quantum machine learning and its applications. ArXiv, abs/2201.04093, https://doi.org/10.48550/arXiv.2201.04093
  • Garg, A., & Mago, V. K. (2021). Role of machine learning in medical research: A survey. Computer Science Review, 40, 100370. https://doi.org/10.1016/j.cosrev.2021.100370
  • Golchha, R., & Verma, G. K. (2023). Quantum-Enhanced Support Vector Classifier for Image Classification. In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), 1-6, IEEE.
  • Gong, Y., Li, K. C., Xiao, L., Cai, J., Xiao, J., Liang, W., & Khan, M. K. (2023). VASERP: An adaptive, lightweight, secure, and efficient RFID-based authentication scheme for IoV. Sensors, 23, 5198. https://doi.org/10.3390/s23115198
  • Grover, L. K. (1997). Quantum computers can search arbitrarily large databases by a single query. Physical Review Letters, 79(23), 4709–4712. https://doi.org/10.1103/PhysRevLett.79.4709
  • Gruska, J., Qiu, D., & Zheng, S. (2014). Generalizations of the distributed Deutsch–Jozsa promise problem. Mathematical Structures in Computer Science, 27(3), 311–331. https://doi.org/10.1017/S0960129515000158
  • Gupta, R., Saxena, D., Gupta, I., Makkar, A., & Kumar Singh, A. (2022). Quantum machine learning driven malicious user prediction for cloud network communications. IEEE Networking Letters, 4(4), 174–178. https://doi.org/10.1109/LNET.2022.3200724
  • Hahn, F., Dahlberg, A., Eisert, J., & Pappa, A. (2022). Limitations of nearest-neighbor quantum networks. Physical Review A, 106(1), L010401. https://doi.org/10.1103/PhysRevA.106.L010401
  • Harrow, A. W., Hassidim, A., & Lloyd, S. (2008). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502. https://doi.org/10.1103/PhysRevLett.103.150502
  • Havenstein, C. L., Thomas, D. T., & Chandrasekaran, S. (2018). Comparisons of performance between quantum and classical machine learning. SMU Data Science Review.
  • Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212. https://doi.org/10.1038/s41586-019-0980-2
  • He, C., Li, J., Liu, W., Peng, J., & Wang, Z. J. (2022). A low-complexity quantum principal component analysis algorithm. IEEE Transactions on Quantum Engineering, 3, 1–13. https://doi.org/10.1109/TQE.2021.3140152
  • Hou, M., Zhang, S., & Xia, J. (2022). Quantum fuzzy K-means algorithm based on fuzzy theory. In International conference on adaptive and intelligent systems (pp. 348–356). Springer International Publishing. https://doi.org/10.1007/978-3-031-06794-5_28.
  • Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022). Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision. Expert Systems with Applications, 194, 116512. https://doi.org/10.1016/j.eswa.2022.116512
  • Hu, W., Fan, J., Du, Y., Li, B., Xiong, N., & Bekkering, E. (2020). MDFC–resnet: An agricultural IoT system to accurately recognize crop diseases. IEEE Access, 8, 115287–115298. https://doi.org/10.1109/ACCESS.2020.3001237
  • Huang, H. Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., & McClean, J. R. (2022). Quantum advantage in learning from experiments. Science, 376(6598), 1182–1186. https://doi.org/10.1126/science.abn7293
  • Huang, Y. M., Lei, H., & Li, X. Y. (2018). A survey on quantum machine learning. Chinese Journal of Computers, 41(1), 145–163.
  • Innan, N., Khan, M. A., Panda, B., & Bennai, M. (2023). Enhancing quantum support vector machines through variational kernel training. ArXiv, abs/2305.06063, https://doi.org/10.48550/arXiv.2305.06063
  • Jadhav, A., Rasool, A., & Gyanchandani, M. (2023). Quantum machine learning: Scope for real-world problems. Procedia Computer Science, 218, 2612–2625. https://doi.org/10.1016/j.procs.2023.01.235
  • Jeswal, S. K., & Chakraverty, S. (2019). Recent developments and applications in quantum neural network: A review. Archives of Computational Methods in Engineering, 26, 793–807. https://doi.org/10.1007/s11831-018-9269-0
  • Kak, S. C. (1995). Quantum neural computing. Advances in Imaging and Electron Physics, 94, 259–313. https://doi.org/10.1016/S1076-5670(08)70147-2
  • Kang, L., Chen, R. S., Xiong, N., Chen, Y. C., Hu, Y. X., & Chen, C. M. (2019). Selecting hyper-parameters of Gaussian process regression based on Non-inertial particle swarm optimization in internet of things. IEEE Access, 7, 59504–59513. https://doi.org/10.1109/ACCESS.2019.2913757
  • Kanimozhi, T., Sridevi, S., Manikumar, T. S., Dheeraj, T., & Sumanth, A. (2022, February). Brain tumor recognition based on classical to quantum transfer learning. In 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), 1-5, IEEE.
  • Kavitha, S. S., & Kaulgud, N. (2021). Quantum K-means clustering method for detecting heart disease using quantum circuit approach. Soft Computing, 27, 1–14. https://doi.org/10.1007/s00500-022-07200-x
  • Kerenidis, I., Prakash, A., & Szilágyi, D. (2019). Quantum algorithms for second-order cone programming and support vector machines. Quantum, 5, 427. https://doi.org/10.22331/q-2021-04-08-427
  • Khadiev, K., Mannapov, I., & Safina, L. (2019). The quantum version of classification decision tree constructing algorithm C5. 0. arXiv preprint arXiv:1907. 06840, https://doi.org/10.48550/arXiv.1907.06840
  • Koch, D., Martin, B., Patel, S., Wessing, L., & Alsing, P. M. (2020). Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer. AIP Advances, 10(9). https://doi.org/10.1063/5.0015526
  • Li, J., Lin, S., Yu, K. H., & Guo, G. (2021). Quantum K-nearest neighbor classification algorithm based on Hamming distance. Quantum Information Processing, 21. https://doi.org/10.1007/s11128-021-03361-0
  • Li, Y. (2022). Research on key problems and algorithms of Quantum machine learning [D]. Shanghai Maritime University.
  • Li, Y., Liang, W., Xie, K., Zhang, D., Xie, S., & Li, K. C. (2023). Lightnestle: Quick and accurate neural sequential tensor completion via meta learning. IEEE INFOCOM, 2023, 1–10.
  • Li, Y., Liu, H., Pan, S., Qin, S., Gao, F., Sun, D. X., & Wen, Q. Y. (2023). Quantum k-medoids algorithm using parallel amplitude estimation. Physical Review A, 107, https://doi.org/10.1103/PhysRevA.107.022421
  • Li, Y., Zhou, R. G., Xu, R. G., Luo, J., & Hu, W. (2020). A quantum deep convolutional neural network for image recognition. Quantum Science & Technology, 5.
  • Li, Z., Chai, Z., Guo, Y., Ji, W., Wang, M., Shi, F., & Du, J. (2021). Resonant quantum principal component analysis. Science Advances, 7(34), eabg2589. https://doi.org/10.1126/sciadv.abg2589
  • Li, Z., Liu, X., Xu, N., & Du, J. (2015). Experimental realization of a quantum support vector machine. Physical Review Letters, 114(14), 140504. https://doi.org/10.1103/PhysRevLett.114.140504
  • Liang, W., Li, Y., Xie, K., Zhang, D., Li, K. C., Souri, A., & Li, K. C. (2023). Spatial-Temporal aware inductive graph neural network for C-ITS data recovery. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8431–8442. https://doi.org/10.1109/TITS.2022.3156266
  • Liang, W., Li, Y., Xu, J., Qin, Z., Zhang, D., & Li, K. C. (2024). QoS prediction and adversarial attack protection for distributed services under DLaaS. IEEE Transactions on Computers, 73(3), 669–682. https://doi.org/10.1109/TC.2021.3077738
  • Liang, W., Xie, S., Li, K. C, Li, X., Kui, X., & Zomaya, A. Y. (2024). MC-DSC: A dynamic secure resource configuration scheme based on medical consortium blockchain. IEEE Transactions on Information Forensics and Security, 1–1. https://doi.org/10.1109/TIFS.2024.3364370
  • Liang, W., Yang, Y., Yang, C., Hu, Y., Xie, S., Li, K. C., & Cao, J. (2023). PDPChain: A consortium blockchain-based privacy protection scheme for personal data. IEEE Transactions on Reliability, 72(2), 586–598. https://doi.org/10.1109/TR.2022.3190932
  • Liang, Z., Wang, Z., Yang, J., Yang, L., Xiong, J., Shi, Y., & Jiang, W. (2021). Can noise on Qubits be learned in quantum neural network? A case study on quantumflow (Invited Paper). 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-7.
  • Lin, J., Bao, W., Zhang, S., Li, T., & Wang, X. (2019). An improved quantum principal component analysis algorithm based on the quantum singular threshold method. Physics Letters A, 383(24), 2862–2868. https://doi.org/10.1016/j.physleta.2019.06.026
  • Liu, H., Zhang, S., Li, M., Liang, W., & Arthur Sandor, V. K. (2023). A real‐time privacy‐preserving scheme based on grouping queries for continuous location‐based services. Concurrency and Computation: Practice and Experience, 35(19). https://doi.org/10.1002/cpe.v35.19
  • Liu, Y., Liang, W., Xie, K., Xie, S., Li, K., & Meng, W. (2024). LightPay: A lightweight and secure off-chain multi-path payment scheme based on adapter signatures. IEEE Transactions on Services Computing, 1–14. https://doi.org/10.1109/TSC.2023.3333806
  • Lloyd, S., Mohseni, M., & Rebentrost, P. (2013a). Quantum algorithms for supervised and unsupervised machine learning. arXiv: Quantum Physics, https://doi.org/10.48550/arXiv.1307.0411
  • Lloyd, S., Mohseni, M., & Rebentrost, P. (2013b). Quantum principal component analysis. Nature Physics, 10(9), 631–633. https://doi.org/10.48550/arXiv.1307.0411
  • Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., & Killoran, N. (2020). Quantum embeddings for machine learning. arXiv preprint arXiv:2001. 03622, https://doi.org/10.48550/arXiv.2001.03622
  • Long, J., Liang, W., Li, K. C., Wei, Y., & Marino, M. D. (2023). A regularized cross-layer ladder network for intrusion detection in industrial internet of things. IEEE Transactions on Industrial Informatics, 19(2), 1747–1755. https://doi.org/10.1109/TII.2022.3204034
  • Lu, S., & Braunstein, S. L. (2014). Quantum decision tree classifier. Quantum Information Processing, 13(3), 757–770. https://doi.org/10.1007/s11128-013-0687-5
  • McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2018). Barren plateaus in quantum neural network training landscapes. Nature Communications, 9(1), 4812. https://doi.org/10.1038/s41467-018-07090-4
  • Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018). Quantum circuit learning. Physical Review A, 98(3), 032309. https://doi.org/10.1103/PhysRevA.98.032309
  • Nagata, K., Nakamura, T., & Farouk, A. M. (2017). Quantum cryptography based on the Deutsch-Jozsa algorithm. International Journal of Theoretical Physics, 56(9), 2887–2897. https://doi.org/10.1007/s10773-017-3456-x
  • Naqa, E. I., & Murphy, M. J. (2015). What Is machine learning. In Machine learning in radiation oncology (pp. 3–11). https://doi.org/10.1007/978-3-319-18305-3_1.
  • Ohno, H. (2022). A quantum algorithm of K-means toward practical use. Quantum Information Processing, 21(4), 146. https://doi.org/10.1007/s11128-022-03485-x
  • Pan, X., Lu, Z., Wang, W., Hua, Z., Xu, Y., Li, W., & Sun, L. (2023). Deep quantum neural networks on a superconducting processor. Nature Communications, 14(1), 4006. https://doi.org/10.1038/s41467-023-39785-8
  • Poggiali, A., Berti, A., Bernasconi, A., Corso, G. D., & Guidotti, R. (2022b). Quantum clustering with k-means: A hybrid approach. ArXiv, abs/2212.06691, https://doi.org/10.48550/arXiv.2212.06691
  • Poggiali, A., Berti, A., Bernasconi, A., Corso, G. M., & Guidotti, R. (2022a). Clustering Classical Data with Quantum k-Means. Italian Conference on Theoretical Computer Science.
  • Quezada, L. F., Sun, G. H., & Dong, S. H. (2022). Quantum version of the k-NN classifier based on a quantum sorting algorithm. Annalen der Physik, 534(5), 2100449. https://doi.org/10.1002/andp.202100449
  • Rebentrost, P., Bromley, T. R., Weedbrook, C., & Lloyd, S. (2018). Quantum Hopfield neural network. Physical Review A, 98(4), 042308. https://doi.org/10.1103/PhysRevA.98.042308
  • Rebentrost, P., Mohseni, M., & Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113(13), 130503. https://doi.org/10.1103/PhysRevLett.113.130503
  • Sagingalieva, A., Kordzanganeh, M., Kenbayev, N., Kosichkina, D., Tomashuk, T., & Melnikov, A. (2022). Hybrid quantum neural network for drug response prediction. Cancers, 15, 2705. https://doi.org/10.3390/cancers15102705
  • Sarmina, B. G., Sun, G. H., & Dong, S. H. (2023). Principal component analysis and t-distributed stochastic neighbor embedding analysis in the study of quantum approximate optimization algorithm entangled and non-entangled mixing operators. Entropy, 25(11), 1499. https://doi.org/10.3390/e25111499
  • Schuld, M., & Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122(4), 040504. https://doi.org/10.1103/PhysRevLett.122.040504
  • Schuld, M., Sinayskiy, I., & Petruccione, F. (2014). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172–185. https://doi.org/10.1080/00107514.2014.964942
  • Sergioli, G., Giuntini, R., & Freytes, H. (2019). A new quantum approach to binary classification. PLoS One, 14(5), e0216224. https://doi.org/10.48550/arXiv.2106.15572
  • Sergioli, G., Santucci, E., Didaci, L., Miszczak, J. A., & Giuntini, R. (2017). A quantum-inspired version of the nearest mean classifier. Soft Computing, 22(3), 691–705. https://doi.org/10.1007/s00500-016-2478-2
  • Shen, Y., Fang, Z., Gao, Y., Xiong, N. N., Zhong, C., & Tang, X. (2019). Coronary arteries segmentation based on 3D FCN With attention gate and level Set function. IEEE Access, 7, 42826–42835. https://doi.org/10.1109/ACCESS.2019.2908039
  • Shor, P. W. (1995). Polynomial-Time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 41(2), 303–332. https://doi.org/10.1137/S0036144598347011
  • Simoes, R. D. M., Huber, P., Meier, N., Smailov, N., Füchslin, R. M., & Stockinger, K. (2023). Experimental evaluation of quantum machine learning algorithms. IEEE Access, 11, 6197–6208. https://doi.org/10.1109/ACCESS.2023.3236409
  • Thanasilp, S., Wang, S., Nghiem, N. A., Coles, P. J., & Cerezo, M. (2021). Subtleties in the trainability of quantum machine learning models. Quantum Machine Intelligence, 5. https://doi.org/10.1007/s42484-023-00103-6
  • Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2015). Hierarchical and networked vehicle surveillance in ITS: A survey. IEEE Transactions on Intelligent Transportation Systems, 18(1), 25–48. https://doi.org/10.1109/TITS.2016.2552778
  • UlHaq, A., & Bonny, T. (2020). Cancer transcriptome analysis with rna-seq using quantum k-means clustering algorithm. In IEEE International Conference on Engineering Innovations in Healthcare.
  • Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530(7589), 144–147. https://doi.org/10.1038/530144a
  • Wan, K. H., Dahlsten, O. C., Kristjánsson, H., Gardner, R., & Kim, M. S. (2016). Quantum generalisation of feedforward neural networks. npj Quantum Information, 3, 1–8. https://doi.org/10.1038/s41534-017-0032-4
  • Wan, Z., Xiong, N., Ghani, N., Vasilakos, A. V., & Zhou, L. (2014). Adaptive unequal protection for wireless video transmission over IEEE 802.11e networks. Multimedia Tools and Applications, 72(1), 541–571. https://doi.org/10.1007/s11042-013-1378-z
  • Wang, J., Jin, C., Tang, Q., Xiong, N. N., & Srivastava, G. (2021). Intelligent ubiquitous network accessibility for wireless-powered MEC in UAV-assisted B5G. IEEE Transactions on Network Science and Engineering, 8(4), 2801–2813. https://doi.org/10.1109/TNSE.2020.3029048
  • Wang, S., Fontana, E., Cerezo, M., Sharma, K., Sone, A., Cincio, L., & Coles, P. J. (2021). Noise-induced barren plateaus in variational quantum algorithms. Nature Communications, 12(1), 6961. https://doi.org/10.1038/s41467-021-27045-6
  • Wang, Y., Fang, W., Ding, Y., & Xiong, N. N. (2021). Computation offloading optimization for UAV-assisted mobile edge computing: A deep deterministic policy gradient approach. Wireless Networks, 27(4), 2991–3006. https://doi.org/10.1007/s11276-021-02632-z
  • Wei, L., Liu, H., Xu, J., Shi, L., Shan, Z., Zhao, B., & Gao, Y. (2023). Quantum machine learning in medical image analysis: A survey. Neurocomputing, 525, 42–53. https://doi.org/10.1016/j.neucom.2023.01.049
  • Wiebe, N., Kapoor, A., & Svore, K. M. (2014a). Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning. Quantum Information & Computation, 15(3&4), 316–356. https://doi.org/10.48550/arXiv.1401.2142
  • Wiebe, N., Kapoor, A., & Svore, K. M. (2014b). Quantum deep learning. ArXiv, abs/1412.3489, https://doi.org/10.48550/arXiv.1412.3489
  • Willsch, D., Willsch, M., De Raedt, H., & Michielsen, K. (2020). Support vector machines on the D-wave quantum annealer. Computer Physics Communications, 248, 107006. https://doi.org/10.1016/j.cpc.2019.107006
  • Wittek, P. (2014). Quantum machine learning: What quantum computing means to data mining.
  • Wossnig, L., Zhao, Z., & Prakash, A. (2018). Quantum linear system algorithm for dense matrices. Physical Review Letters, 120. https://doi.org/10.1103/PhysRevLett.120.050502
  • Wu, C., Ju, B., Wu, Y., Lin, X., Xiong, N., Xu, G., Li, H., & Liang, X. (2019). UAV autonomous target search based on deep reinforcement learning in complex disaster scene. IEEE Access, 7, 117227–117245. https://doi.org/10.1109/ACCESS.2019.2933002
  • Wu, Z., Song, T., & Zhang, Y. (2021). Quantum k-means algorithm based on manhattan distance. Quantum Information Processing, 21. https://doi.org/10.1007/s11128-021-03384-7
  • Xie, S., Xiao, L., Han, D., Xie, K., Li, X., & Liang, W. (2024). HCVC: A high-capacity off-chain virtual channel scheme based on bidirectional locking mechanism. IEEE Transactions on Network Science and Engineering, 1–12. https://doi.org/10.1109/TNSE.2023.3332130
  • Xu, Z., Liang, W., Li, K. C., Xu, J., Zomaya, A. Y., & Zhang, J. (2022). A time-sensitive token-based anonymous authentication and dynamic group Key agreement scheme for industry 5.0. IEEE Transactions on Industrial Informatics, 18(10), 7118–7127. https://doi.org/10.1109/TII.2021.3129631
  • Yang, J., Awan, A. J., & Vall-Llosera, G. (2019). Support vector machines on noisy intermediate scale quantum computers. arXiv preprint arXiv, 1909, 11988. https://doi.org/10.48550/arXiv.1909.11988
  • Yang, Q., Zhu, X., Wang, X., Fu, J., Zheng, J., & Liu, Y. (2023). A novel authentication and key agreement scheme for Internet of Vehicles. Future Generation Computer Systems, 145, 415–428. https://doi.org/10.1016/j.future.2023.03.037
  • Ye, Z., Li, L., Situ, H., & Wang, Y. (2020). Quantum speedup of twin support vector machines. Science China Information Sciences, 63, 1–3. https://doi.org/10.48550/arXiv.1902.08907
  • Zardini, E., Blanzieri, E., & Pastorello, D. (2023). A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation. ArXiv, abs/2305.04287, https://doi.org/10.48550/arXiv.2305.04287
  • Zeguendry, A., Jarir, Z., & Quafafou, M. (2023). Quantum machine learning: A review and case studies. Entropy, 25(2), 287. https://doi.org/10.3390/e25020287
  • Zhang, B., Wang, X., Xie, R., Li, C., Zhang, H., & Jiang, F. (2023). A reputation mechanism based Deep Reinforcement Learning and blockchain to suppress selfish node attack motivation in Vehicular Ad-Hoc Network. Future Generation Computer Systems, 139, 17–28. https://doi.org/10.1016/j.future.2022.09.010
  • Zhang, Q., Sun, H., Wu, X., & Zhong, H. (2019). Edge video analytics for public safety: A review. Proceedings of the IEEE, 107(8), 1675–1696. https://doi.org/10.1109/JPROC.2019.2925910
  • Zhang, R., Wang, J., Jiang, N., Li, H., & Wang, Z. (2022). Quantum support vector machine based on regularized newton method. Neural Networks, 151, 376–384. https://doi.org/10.1016/j.neunet.2022.03.043
  • Zhang, S., He, J., Liang, W., & Li, K. (2024). MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing. Future Generation Computer Systems, 151, 32–44. https://doi.org/10.1016/j.future.2023.09.023
  • Zhang, S., Hu, B., Liang, W., Li, K. C., & Pathan, A. S. K. (2024). A trajectory privacy-preserving scheme based on transition matrix and caching for IIoT. IEEE Internet of Things Journal, 11(4), 5745–5756. https://doi.org/10.1109/JIOT.2023.3308073
  • Zhang, S., Li, M., Liang, W., Sandor, V. K. A., & Li, X. (2022). A survey of dummy-based location privacy protection techniques for location-based services. Sensors, 22(16), 6141. https://doi.org/10.3390/s22166141
  • Zhang, W., Zhu, S., Tang, J. L., & Xiong, N. N. (2018). A novel trust management scheme based on dempster–shafer evidence theory for malicious nodes detection in wireless sensor networks. The Journal of Supercomputing, 74(4), 1779–1801. https://doi.org/10.1007/s11227-017-2150-3
  • Zhang, Y., & Ni, Q. (2020). Recent advances in quantum machine learning. Quantum Engineering, 2. https://doi.org/10.1002/que2.34
  • Zhou, M. G., Liu, Z. P., Yin, H. L., Li, C. L., Xu, T. K., & Chen, Z. B. (2023). Quantum neural network for quantum neural computing. Research; A Journal of Science and Its Applications, 6, 0134. https://doi.org/10.34133/research.0134
  • Zhou, N. R., Liu, X. X., Chen, Y. L., & Du, N. S. (2021). Quantum K-nearest-neighbor image classification algorithm based on KL transform. International Journal of Theoretical Physics, 60(3), 1209–1224. https://doi.org/10.1007/s10773-021-04747-7
  • Zhou, S., Li, K. C., Xiao, L., Cai, J., Liang, W., & Castiglione, A. (2023). A systematic review of consensus mechanisms in blockchain. Mathematics, 11(10), 2248. https://doi.org/10.3390/math11102248
  • Zhou, Y., Zhang, Y., Liu, H., Xiong, N., & Vasilakos, A. V. (2014). A bare-metal and asymmetric partitioning approach to client virtualization. IEEE Transactions on Services Computing, 7(1), 40–53. https://doi.org/10.1109/TSC.2012.32