Quantum computing in intelligent transportation systems: A survey AITranslate

[1]L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, "Big data analytics in intelligent transportation systems: A survey," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 383–398, 2018.

[2]S. Wang, Z. Pei, C. Wang, and J. Wu, "Shaping the future of the application of quantum computing in intelligent transportation system," Intelligent and Converged Networks, vol. 2, no. 4, pp. 259–276, 2021.

[3]N. Abdelgaber and C. Nikolopoulos, "Overview on quantum com¬puting and its applications in artificial intelligence,"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Irvine, CA, USA, 2020, pp. 198–199.

[4]K. Michielsen, M. Nocon, D. Willsch, F. Jin, T. Lippert, and H. De Raedt, "Benchmarking gate-based quantum computers," Computer Physics Communications, vol. 220, pp. 44–55, 2017.

[5]P. W. Shor, "Algorithms for quantum computation: Discrete logarithms and factoring,"in Proceedings 35th Annual SymPosium on Foundations of Computer Science, Santa Fe, NM, USA, 1994, pp. 124–134.

[6]L. K. Grover, "A fast quantum mechanical algorithm for database search,"in Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, Philadelphia, Pennsylvania, USA, 1996, pp. 212–219.

[7]A. B. Finnila, M. A. Gomez, C. Sebenik, C. Stenson, and J. D. Doll, "Quantum annealing: A new method for minimizing multidimensional functions," Chemical Physics Letters, vol. 219, no. 5–6, pp. 343–348, 1994.

[8]E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, "Quantum computation by adiabatic evolution," arXiv preprint quant-ph/0001106, 2000.

[9]J. Roland and N. J. Cerf, "Quantum search by local adiabatic evolution," Physical Review A, vol. 65, no. 4, p. 042308, 2002.

[10]V. V. Dixit and C. Niu, "Quantum computing for transport network design problems," Scientific Reports, vol. 13, no. 1, p. 12267, 2023.

[11]Y. Aharonov, L. Davidovich, and N. Zagury, "Quantum random walks," Physical Review A, vol. 48, no. 2, p. 1687, 1993.

[12]J. Kempe, "Quantum random walks: An introductory overview," Contemporary Physics, vol. 44, no. 4, pp. 307–327, 2003.

[14]A. Holmes, M. R. Jokar, G. Pasandi, Y. Ding, M. Pedram, and F. T. Chong, "NISQ+: Boosting quantum computing power by approximating quantum error correction,"2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain, 2020, pp. 556–569.

[15]E. Dilek and M. Dener, "Computer vision applications in intelligent transportation systems: A survey," Sensors, vol. 23, no. 6, p. 2938, 2023.

[16]H. Yuan and G. Li, "A survey of traffic prediction: From spatio-temporal data to intelligent transportation," Data Science and Engineering, vol. 6, no. 1, pp. 63–85, 2021.

[17]P. Patil, "Applications of deep learning in traffic management: A review," International Journal of Business Intelligence and Big Data Analytics, vol. 5, no. 1, pp. 16–23, 2022.

[18]J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, "Quantum machine learning," Nature, vol. 549, no. 7671, pp. 195–202, 2017.

[20]K. Batra, K. M. Zorn, D. H. Foil, E. Minerali, V. O. Gawriljuk, T. R. Lane, and S. Ekins, "Quantum machine learning algorithms for drug discovery applications," Journal of Chemical Information and Modeling, vol. 61, no. 6, pp. 2641–2647, 2021.

[21]M. Schuld, I. Sinayskiy, and F. Petruccione, "An introduction to quantum machine learning," Contemporary Physics, vol. 56, no. 2, pp. 172–185, 2015.

[22]S. B. Ramezani, A. Sommers, H. K. Manchukonda, S. Rahimi, and A. Amirlatifi, "Machine learning algorithms in quantum computing: A survey,"in 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.

[23]A. Narayanan and T. Menneer, "Quantum artificial neural network architectures and components," Information Sciences, vol. 128, no. 3–4, pp. 231–255, 2000.

[24]M. Schuld, I. Sinayskiy, and F. Petruccione, "Simulating a perceptron on a quantum computer," Physics Letters A, vol. 379, no. 7, pp. 660–663, 2015.

[25]Y. Cao, G. G. Guerreschi, and A. Aspuru-Guzik, "Quantum neuron: An elementary building block for machine learning on quantum computers," arXiv preprint arXiv:1711.11240, 2017.

[26]M. Panella and G. Martinelli, "Neurofuzzy networks with nonlinear quantum learning," IEEE Transactions on Fuzzy Systems, vol. 17, no. 3, pp. 698–710, 2008.

[27]K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, and R. Wolf, "Training deep quantum neural networks," Nature Communications, vol. 11, no. 1, p. 808, 2020.

[28]S. Oh, J. Choi, and J. Kim, "A tutorial on quantum convolutional neural networks (QCNN),"2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, South Korea, 2020, pp. 236–239.

[29]I. Kerenidis, J. Landman, and A. Prakash, "Quantum algorithms for deep convolutional neural networks," arXiv preprint arXiv:1911.01117, 2019.

[30]I. Kerenidis and A. Prakash, "Quantum gradient descent for linear systems and least squares," Physical Review A, vol. 101, no. 2, p. 022316, 2020.

[31]M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein generative adversarial networks,"34th International Conference on Machine Learning, Sydney, Australia, 2017, pp. 214–223.

[32]S. Chakrabarti, H. Yiming, T. Li, S. Feizi, and X. Wu, "Quantum wasserstein generative adversarial networks,"33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 2019.

[33]H. Situ, Z. He, Y. Wang, L. Li, and S. Zheng, "Quantum generative adversarial network for generating discrete distribution," Information Sciences, vol. 538, pp. 193–208, 2020.

[34]A. Anand, P. Schleich, S. Alperin-Lea, P. W. Jensen, S. Sim, M. D´ıaz-Tinoco, J. S. Kottmann, M. Degroote, A. F. Izmaylov, and A. Aspuru-Guzik, "A quantum computing view on unitary coupled cluster theory," Chemical Society Reviews, vol. 51, no. 5, pp. 1659–1684, 2022.

[35]D. Horn and A. Gottlieb, "The method of quantum clustering,"in Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver British Columbia, Canada, 2001, pp. 769–776.

[36]E. Aïmeur, G. Brassard, and S. Gambs, "Quantum clustering algorithms,"in Proceedings of the 24th International Conference on Machine Learning, Corval is Oregon, USA2007, pp. 1–8.

[37]S. Lloyd, M. Mohseni, and P. Rebentrost, "Quantum algorithms for supervised and unsupervised machine learning," arXiv preprint arXiv:1307. 0411, 2013.

[38]A. M. De Souza, C. A. Brennand, R. S. Yokoyama, E. A. Donato, E. R. Madeira, and L. A. Villas, "Traffic management systems: A classifica¬tion, review, challenges, and future perspectives," International Journal of Distributed Sensor Networks, vol. 13, no. 4, p. 1550147716683612, 2017.

[39]C. Iliopoulou and K. Kepaptsoglou, "Combining its and optimization in public transportation planning: State of the art and future research paths," European Transport Research Review, vol. 11, art. no. 27, 2019.

[40]F. Zhang, T.-Y. Wu, Y. Wang, R. Xiong, G. Ding, P. Mei, and L. Liu, "Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction," IEEE Access, vol. 8, pp. 104555–104564, 2020.

[41]T. Kohonen and T. Kohonen, "Learning vector quantization," Self-Organizing Maps, pp. 245–261, 2001.

[42]R. Lahoz-Beltra, "Quantum genetic algorithms for computer scientists," Computers, vol. 5, no. 4, p. 24, 2016.

[43]D. Zhang, J. Wang, H. Fan, T. Zhang, J. Gao, and P. Yang, "New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system," International Journal of Communication Systems, vol. 34, no. 1, p. e4647, 2021.

[44]W. Huang, J. Zhang, S. Liang, and H. Sun, "Backbone network traffic prediction based on modified EEMD and quantum neural network," Wireless Personal Communications, vol. 99, pp. 1569–1588, 2018.

[45]Z. Qu, X. Liu, and M. Zheng, "Temporal-spatial quantum graph convolutional neural network based on Schrödinger approach for traffic congestion prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, 8677–8686, 2022.

[46]B. Yu, H. Yin, and Z. Zhu, "Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting," arXiv preprint arXiv:1709.04875, 2017.

[47]W. Hu, H. Wang, Z. Qiu, C. Nie, and L. Yan, "A quantum particle swarm optimization driven urban traffic light scheduling model," Neural Computing and Applications, vol. 29, pp. 901–911, 2018.

[48]H. Hussain, M. B. Javaid, F. S. Khan, A. Dalal, and A. Khalique, "Optimal control of traffic signals using quantum annealing," Quantum Information Processing, vol. 19, pp. 1–18, 2020.

[49]F. Neukart, G. Compostella, C. Seidel, D. Von Dollen, S. Yarkoni, and B. Parney, "Traffic flow optimization using a quantum annealer," Frontiers in ICT, vol. 4, p. 29, 2017.

[50]D. Inoue, A. Okada, T. Matsumori, K. Aihara, and H. Yoshida, "Traffic signal optimization on a square lattice with quantum annealing," Scientific Reports, vol. 11, no. 1, p. 3303, 2021.

[51]T. Garaix, C. Artigues, D. Feillet, and D. Josselin, "Vehicle routing problems with alternative paths: An application to on-demand transportation," European Journal of Operational Research, vol. 204, no. 1, pp. 62–75, 2010.

[52]M. Nama, A. Nath, N. Bechra, J. Bhatia, S. Tanwar, M. Chaturvedi, and B. Sadoun, "Machine learning-based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges," International Journal of Communication Systems, vol. 34, no. 9, p. e4814, 2021.

[53]H. R. Moser, "The quantum mechanical solution of the traveling salesman problem," Physica E: Low-Dimensional Systems and Nanostructures, vol. 16, no. 2, pp. 280–285, 2003.

[54]R. Martoňák, G. E. Santoro, and E. Tosatti, "Quantum annealing of the traveling-salesman problem," Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 70, no. 5, p. 057701, 2004.

[55]H. Chen, X. Kong, B. Chong, G. Qin, X. Zhou, X. Peng, and J. Du, "Experimental demonstration of a quantum annealing algorithm for the traveling salesman problem in a nuclear-magnetic-resonance quantum simulator," Physical Review A, vol. 83, no. 3, p. 032314, 2011.

[56]A. E. Moylett, N. Linden, and A. Montanaro, "Quantum speedup of the traveling-salesman problem for bounded-degree graphs," Physical Review A, vol. 95, no. 3, p. 032323, 2017.

[57]S. Dörn, "Quantum algorithms for optimal graph traversal problems," SPIE Defende + Commercial Sensing, SPIE, 2007.

[58]E. Osaba, E. Villar-Rodriguez, and I. Oregi, "A systematic literature review of quantum computing for routing problems," IEEE Access, vol. 10, pp. 55805–55817, 2022.

[59]S. Zajac and S. Huber, "Objectives and methods in multi-objective routing problems: A survey and classification scheme," European Journal of Operational Research, vol. 290, no. 1, pp. 1–25, 2021.

[60]J. Zhang, W. Wang, Y. Zhao, and C. Cattani, "Multiobjective quantum evolutionary algorithm for the vehicle routing problem with customer satisfaction," Mathematical Problems in Engineering, vol. 2012, no. 1, art ID 879614, 2012.

[61]Q. Zhang, S. Liu, D. Gong, H. Zhang, and Q. Tu, "An improved multi-objective quantum-behaved particle swarm optimization for railway freight transportation routing design," IEEE Access, vol. 7, pp. 157353–157362, 2019.

[62]L. Wang, S. Kowk, and W. Ip, "Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems," Journal of Intelligent Manufacturing, vol. 23, pp. 2227–2236, 2012.

[63]K.-H. Han and J.-H. Kim, "Quantum-inspired evolutionary algorithm for a class of combinatorial optimization," IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 580–593, 2002.

[64]I. D. Leonidas, A. Dukakis, B. Tan, and D. G. Angelakis, "Qubit efficient quantum algorithms for the vehicle routing problem on quantum computers of the NISQ era," arXiv preprint arXiv:2306.08507, 2023.

[65]S. Feld, C. Roch, T. Gabor, C. Seidel, F. Neukart, I. Galter, W. Mauerer, and C. Linnhoff-Popien, "A hybrid solution method for the capacitated vehicle routing problem using a quantum annealer," Frontiers in ICT, vol. 6, p. 13, 2019.

[66]E. Pelofske, G. Hahn, and H. N. Djidjev, "Parallel quantum annealing," Scientific Reports, vol. 12, no. 1, p. 4499, 2022.

[67]G. Laporte and F. Semet, "Classical heuristics for the capacitated VRP,"in The Vehicle Routing Problem, Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2002, pp. 109–128.

[68]H. Irie, G. Wongpaisarnsin, M. Terabe, A. Miki, and S. Taguchi, "Quantum annealing of vehicle routing problem with time, state and capacity,"Quantum Technology and Optimization Problems: First International Workshop, QTOP 2019, Munich, Germany, 2019, 145–156.

[69]U. Azad, B. K. Behera, E. A. Ahmed, P. K. Panigrahi, and A. Farouk, "Solving vehicle routing problem using quantum approximate optimization algorithm," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 7564–7573, 2022.

[70]O. Burkacky, L. Pautasso, and N. Mohr, "Will quantum computing drive the automotive future," Mckinsey & Company, vol. 1, pp. 33–38, 2020.

[71]H. T. Larasati, T.-T.-H. Le, and H. Kim, "Trends of quantum computing applications to computer vision,"2022 International Conference on Platform Technology and Service (PlatCon), Jeju, South Korea, 2022, pp. 7–12.

[72]L. Chen, S. Teng, B. Li, X. Na, Y. Li, Z. Li, J. Wang, D. Cao, N. Zheng, and F.-Y. Wang, "Milestones in autonomous driving and intelligent vehicles—Part II: Perception and planning," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 10, 2023.

[73]H. Baek, D. Kim, and J. Kim, "Fast quantum convolutional neural networks for low-complexity object detection in autonomous driving applications," arXiv preprint arXiv:2401.01370, 2023.

[74]R. Majumder, S. M. Khan, F. Ahmed, Z. Khan, F. Ngeni, G. Comert, J. Mwakalonge, D. Michalaka, and M. Chowdhury, "Hybrid classical-quantum deep learning models for autonomous vehicle traffic image classification under adversarial attack," arXiv preprint arXiv:2108.01125, 2021.

[75]J. Amin, M. A. Anjum, K. Ibrar, M. Sharif, S. Kadry, and R. G. Crespo, "Detection of anomaly in surveillance videos using quantum convolutional neural networks," Image and Vision Computing, vol. 135, p. 104710, 2023.

[76]M. Henderson, S. Shakya, S. Pradhan, and T. Cook, "Quanvolutional neural networks: Powering image recognition with quantum circuits," Quantum Machine Intelligence, vol. 2, no. 1, p. 2, 2020.

[77]J.-B. Sheu, "A quantum mechanics-based approach to model incidentinduced dynamic driver behavior," Physica D: Nonlinear Phenomena, vol. 237, no. 13, pp. 1800–1814, 2008.

[78]P. D. Bruza, Z. Wang, and J. R. Busemeyer, "Quantum cognition: A new theoretical approach to psychology," Trends in Cognitive Sciences, vol. 19, no. 7, pp. 383–393, 2015.

[79]Q. Song, W. Wang, W. Fu, Y. Sun, D. Wang, and Z. Gao, "Research on quantum cognition in autonomous driving," Scientific Reports, vol. 12, no. 1, p. 300, 2022.

[80]G. Arun and V. Mishra, "A review on quantum computing and communication,"2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking, Surat, India, 2014, pp. 1–5.

[81]H. Xiao, A. T. Chronopoulos, and Z. Zhang, "An efficient security scheme for vehicular communication using a quantum secret sharing method," IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1101–1105, 2019.

[82]M. Islam, M. Chowdhury, Z. Khan, and S. M. Khan, "Hybrid quantum classical neural network for cloud-supported in-vehicle cyberattack detection," IEEE Sensors Letters, vol. 6, no. 4, pp. 1–4, 2022.

[83]A. D. C´orcoles, A. Kandala, A. Javadi-Abhari, D. T. McClure, A. W. Cross, K. Temme, P. D. Nation, M. Steffen, and J. M. Gambetta, "Challenges and opportunities of near-term quantum computing systems," Proceedings of the IEEE, vol. 108, no. 8, pp. 1338–1352, 2019.

[84]N. P. De Leon, K. M. Itoh, D. Kim, K. K. Mehta, T. E. Northup, H. Paik, B. Palmer, N. Samarth, S. Sangtawesin, and D. W. Steuerman, "Materials challenges and opportunities for quantum computing hardware," Science, vol. 372, no. 6539, p. eabb2823, 2021.

[85]D. DiVincenzo and B. Terhal, "Decoherence: The obstacle to quantum computation," Physics World, vol. 11, no. 3, p. 53, 1998.

[86]J. Roffe, "Quantum error correction: an introductory guide," Contemporary Physics, vol. 60, no. 3, pp. 226–245, 2019.

[87]S. Takeda and A. Furusawa, "Toward large-scale fault-tolerant universal photonic quantum computing," APL Photonics, vol. 4, no. 6, 2019.

[88]M. Fingerhuth, T. Babej, and P. Wittek, "Open source software in quantum computing," PLOS ONE, vol. 13, no. 12, p. e0208561, 2018.

[89]S. A. Metwalli and R. Van Meter, "A tool for debugging quantum circuits,"2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, 2022, pp. 624–634.

[90]V. Narasimhachar and G. Gour, "Low-temperature thermodynamics with quantum coherence," Nature Communications, vol. 6, no. 1, p. 7689, 2015.

Basic Information:

DOI：10.23919/CHAIN.2024.000007

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Citation Information:

Quantum computing, a field utilizing the principles of quantum mechanics, promises great advancements across various industries. This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems, exploring its potential to transform areas such as traffic optimization, logistics, routing, and autonomous vehicles. By examining current research efforts, challenges, and future directions, this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation.