AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (5.2 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Shaping the future of the application of quantum computing in intelligent transportation system

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
Department of Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
Show Author Information

Abstract

The intelligent transportation system (ITS) integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries. This paper analyzes the shortcomings of ITS, introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer, and shows how to use quantum advantages to improve the existing ITS. The application of quantum computer in transportation field is reviewed from three application directions: path planning, transportation operation management, and transportation facility layout. Due to the slow development of the current universal quantum computer, the D-Wave quantum machine is used as a breakthrough in the practical application. This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS, emphasizes the importance and necessity of introducing quantum computing into intelligent transportation, and discusses the possible development direction in the future.

References

1

S. Bhupendra and G. Ankit, Recent trends in intelligent transportation systems: A review, Journal of Transport Literature, vol. 9, no. 2, pp. 30–34, 2015.

2

W. Guo, Y. Zhang, and L. Li, The integration of CPS, CPSS, and ITS: A focus on data, Tsinghua Science and Technology, vol. 20, no. 4, pp. 327–335, 2015.

3
X. P. Yan, H. Zhang, and C. Z. Wu, Research and development of intelligent transportation systems, in Proc. 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science, Guilin, China, 2012, pp. 321–327.https://doi.org/10.1109/DCABES.2012.107
4

W. X. Li, G. Y. Wu, Y. Zhang, and M. J. Barth, Safety analysis of the eco-approach and departure application at a signalized corridor, Tsinghua Science and Technology, vol. 23, no. 2, pp. 157–171, 2018.

5

G. S. Khekare and A. V. Sakhare, Intelligent traffic system for VANET: A survey, International Journal of Advanced Computer Research, vol. 2, no. 6, pp. 99–102, 2012.

6
M. S. Satyanarayana, B. M. M. Mohan, and S. N. Raghavendra, Intelligent traffic system to reduce waiting time at traffic signals for vehicle owners, in Artificial Intelligence and Evolutionary Computations in Engineering Systems, S. S. Dash, P. C. B. Naidu, R. Bayindir, and S. Das, eds. Singapore: Springer, 2018, pp. 281–287.https://doi.org/10.1007/978-981-10-7868-2_28
7

L. Z. Zhang, N. R. Alharbe, G. C. Luo, Z. Y. Yao, and Y. Li, A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction, Tsinghua Science and Technology, vol. 23, no. 4, pp. 479–492, 2018.

8

A. K. Agogino and K. Tumer, A multiagent approach to managing air traffic flow, Autonomous Agents&Multi-Agent Systems, vol. 24, no. 1, pp. 1–25, 2012.

9

E. Macioszek, Application of intelligent transport systems in road transport for providing travellers with quick and efficient information, Bju International, vol. 109, no. 8, pp. 1134–1139, 2014.

10

T. Drage, J. Kalinowski, and T. Braunl, Integration of drive-by-wire with navigation control for a driverless electric race car, IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 4, pp. 23–33, 2014.

11

P. Talluri and K. M. Anil, Intelligent traffic system which respond to emergencies, International Journal of Engineering Trends&Technology, vol. 4, no. 4, pp. 1132–1133, 2013.

12

L. Moreira-Matias, J. Gama, M. Ferreira, J. M. Moreira, and L. Damas, Predicting taxi-passenger demand using streaming data, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1393–1402, 2013.

13

G. Díaz, H. Macià, V. Valero, J. Boubeta-Puig, and F. Cuartero, An intelligent transportation system to control air pollution and road traffic in cities integrating CEP and colored petri nets, Neural Computing and Applications, vol. 32, no. 2, pp. 405–426, 2020.

14

S. J. Gan, S. Liang, K. Li, J. Deng, and T. L. Cheng, Long-term ship speed prediction for intelligent traffic signaling, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 82–91, 2017.

15

M. K. Shi, Y. Zhang, D. Y. Yao, and C. Lu, Application-oriented performance comparison of 802.11p and LTE-V in a V2V communication system, Tsinghua Science and Technology, vol. 24, no. 2, pp. 123–133, 2019.

16

W. B. Hu, C. Nie, Z. Y. Qiu, B. Du, and Q. Yuan, A route guidance method based on quantum searching for real-time dynamic multi-intersections in urban traffic networks, (in Chinese), Acta Electronica Sinica, vol. 46, no. 1, pp. 104–109, 2018.

17
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, nos. 5&6, pp. 343–348, 2012.https://doi.org/10.1016/0009-2614(94)00117-0
18

P. Benioff, The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines, Journal of Statistical Physics, vol. 22, no. 5, pp. 563–591, 1980.

19

C. H. Bennett and P. W. Shor, Quantum information theory, IEEE Transactions on Information Theory, vol. 44, no. 6, pp. 2724–2742, 1998.

20

D. Castelvecchi, Quantum computers ready to leap out of the lab in 2017, Nature, vol. 541, no. 7635, pp. 9–10, 2017.

21
F. Hu, B. N. Wang, N. Wang, and C. Wang, Quantum machine learning with D-wave quantum computer, Quantum Engineering, doi: 10.1002/que2.12.https://doi.org/10.1002/que2.12
22

D. Loss and D. P. Divincenzo, Quantum computation with quantum dots, Physical Review A, vol. 57, no. 1, pp. 120–126, 1997.

23

J. F. Poyatos, J. I. Cirac, and P. Zoller, Complete characterization of a quantum process: The two-bit quantum gate, Physical Review Letters, vol. 78, no. 2, pp. 390–393, 1996.

24

R. P. Feynman, Simulating physics with computers, International Journal of Theoretical Physics, vol. 21, no. 6, pp. 467–488, 1982.

25

D. Deutsch, Quantum theory, the church-turing principle and the universal quantum computer, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, vol. 400, no. 1818, pp. 97–117, 1985.

26
P. W. Shor, Algorithms for quantum computation: Discrete logarithms and factoring, in Proc. 35th Annual Symposium on Foundations of Computer Science, Santa Fe, NM, USA, 1994, pp. 124–134.
27

C. Wang, H. N. Yao, B. N. Wang, F. Hu, H. G. Zhang, and X. M. Ji, Progress in quantum computing cryptography attacks, Chinese Journal of Computers, vol. 43, no. 9, pp. 1691–1707, 2020.

28

L. K. Grover, Quantum mechanics helps in searching for a needle in a haystack, Physical Review Letters, vol. 79, no. 2, pp. 325–328, 1997.

29

A. D. Córcoles, E. Magesan, S. J. Srinivasan, A. W. Cross, M. Steffen, J. M. Gambetta, and J. M. Chow, Demonstration of a quantum error detection code using a square lattice of four superconducting qubits, Nature Communications, vol. 6, no. 1, p. 6979, 2015.

30

R. Barends, A. Shabani, L. Lamata, J. Kelly, A. Mezzacapo, U. L. Heras, R. Babbush, A. G. Fowler, B. Campbell, Y. Chen, et al., Digitized adiabatic quantum computing with a superconducting circuit, Nature, vol. 534, no. 7606, pp. 222–226, 2016.

31

J. I. Cirac and P. Zoller, Quantum computation with cold trapped ions, Physical Review Letters, vol. 74, no. 20, pp. 4091–4094, 1995.

32

S. Debnath, N. M. Linke, C. Figgatt, K. A. Landsman, K. Wright, and C. Monroe, Demonstration of a small programmable quantum computer with atomic qubits, Nature, vol. 536, no. 7614, pp. 63–66, 2016.

33

H. Bernien, S. Schwartz, A. Keesling, H. Levine, A. Omran, H. Pichler, S. Choi, A. S. Zibrov, M. Endres, M. Greiner, et al., Probing many-body dynamics on a 51-atom quantum simulator, Nature, vol. 551, no. 7682, pp. 579–584, 2017.

34

J. Zhang, G. Pagano, P. W. Hess, A. Kyprianidis, P. Becker, H. Kaplan, A. V. Gorshkov, Z. X. Gong, and C. Monroe, Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator, Nature, vol. 551, no. 7682, pp. 601–604, 2017.

35

H. Wang, W. Li, X. Jiang, Y. M. He, Y. H. Li, X. Ding, M. C. Chen, J. Qin, C. Z. Peng, C. Schneider, et al., Toward scalable boson sampling with photon loss, Physical Review Letters, vol. 120, no. 23, pp. 40–45, 2018.

36
K. Boothby, P. Bunyk, J. Raymond, and A. Roy, Next-generation topology of D-wave quantum processors, arXiv preprint arXiv: 2003.00133, 2020.
37

E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda, A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem, Science, vol. 292, no. 5516, pp. 472–475, 2001.

38

G. E. Santoro and E. Tosatti, Optimization using quantum mechanics: Quantum annealing through adiabatic evolution, J. Phys. A:Math. Gen., vol. 39, no. 36, pp. 393–431, 2006.

39

J. Brooke, D. Bitko, T. F. Rosenbaum, and G. Aeppli, Quantum annealing of a disordered magnet, Science, vol. 284, no. 5415, pp. 779–781, 1999.

40

G. E. Santoro, R. Martoňák, E. Tosatti, and R. Car, Theory of quantum annealing of an Ising spin glass, Science, vol. 295, no. 5564, pp. 2427–2430, 2002.

41
A. Das and B. K. Chakrabarti, Quantum Annealing and Related Optimization Methods. Berlin, Germany: Springer-Verlag, 2005.https://doi.org/10.1007/11526216
42

A. Das and B. K. Chakrabarti, Colloquium: Quantum annealing and analog quantum computation, Review of Modern Physics, vol. 80, no. 3, pp. 1061–1081, 2008.

43

S. Boixo, T. F. Rønnow, S. V. Isakov, Z. H. Wang, D. Wecker, D. A. Lidar, J. M. Martinis, and M. Troyer, Evidence for quantum annealing with more than one hundred qubits, Nature Physics, vol. 10, no. 3, pp. 218–224, 2014.

44

M. Born and V. A. Fock, Beweis des Adiabatensatzes, Zeitschrift für Physik, vol. 51, no. 3, pp. 165–180, 1928.

45

T. Kato, On the adiabatic theorem of quantum mechanics, Journal of the Physical Society of Japan, vol. 5, no. 6, pp. 435–439, 1950.

46

D. A. Lidar, A. Hamma, and A. T. Rezakhani, Adiabatic approximation with better than exponential accuracy for many-body systems and quantum computation, Journal of Mathematical Physics, vol. 50, no. 10, pp. 102106–102132, 2009.

47

V. Choi, Minor-embedding in adiabatic quantum computation: I. The parameter setting problem, Quantum Information Processing, vol. 7, no. 5, pp. 193–209, 2008.

48

E. Ising, Beitrag zur theorie des ferromagnetismus, Zeitschrift für Physik, vol. 31, no. 1, pp. 253–258, 1925.

49
J. E. Dorband, A method of finding a lower energy solution to a QUBO/Ising objective function, arXiv preprint arXiv: 1801.04849, 2018.
50
D-Wave Systems, D-wave releases hybrid workflow platform to build and run quantum hybrid applications in leap quantum application environment, https://www.dwavesys.com/company/newsroom/press-release/d-wave-releases-hybrid-workflow-platform-to-build-and-run-quantum-hybrid-applications-in-leap-quantum-application-environment/, 2018.
51
D-Wave Systems, D-Wave initiates open quantum software environment, https://www.dwavesys.com/press-releases/d-wave-initiates-open-quantum-software-environment, 2017.
52

A. Mott, J. Job, J. R. Vlimant, D. Lidar, and M. Spiropulu, Solving a Higgs optimization problem with quantum annealing for machine learning, Nature, vol. 550, no. 7676, pp. 375–379, 2017.

53

N. Florian, D. D. Von, and S. Christian, Quantum-assisted cluster analysis on a quantum annealing device, Frontiers in Physics, vol. 6, p. 55, 2018.

54
W. Vinci, L. Buffoni, H. Sadeghi, A. Khoshaman, E. Andriyash, and M. Amin, A path towards quantum advantage in training deep generative models with quantum annealers, Machine Learning Science and Technology, doi: 10.1088/2632-2153/aba220.https://doi.org/10.1088/2632-2153/aba220
55
M. P. Henderson, J. Novak, and T. Cook, Leveraging adiabatic quantum computation for election forecasting, Journal of the Physical Society of Japan, doi: 10.7566/JPSJ.88.061009.https://doi.org/10.7566/JPSJ.88.061009
56

K. Kitai, J. Guo, S. H. Ju, S. Tanaka, K. Tsuda, J. Shiomi, and R. Tamura, Designing metamaterials with quantum annealing and factorization machines, Physical Review Research, vol. 2, no. 1, p. 013319, 2020.

57

R. Y. Li, R. D. Felice, R. Rohs, and D. A. Lidar, Quantum annealing versus classical machine learning applied to a simplified computational biology problem, npj Quantum Information, vol. 4, p. 14, 2018.

58
F. Neukart, G. Compostella, C. Seidel, D. Von Dollen, S. Yarkoni, and B. Parney, Traffic flow optimization using a quantum annealer, Frontiers in ICT, doi: 10.3389/fict.2017.00029.https://doi.org/10.3389/fict.2017.00029
59
D. Inoue, A. Okada, T. Matsumori, K. Aihara, and H. Yoshida, Traffic signal optimization on a square lattice with quantum annealing, arXiv preprint arXiv: 2003.07527, 2020.https://doi.org/10.1038/s41598-021-82740-0
60
R. Y. Li, S. Gujja, S. R. Bajaj, O. E. Gamel, N. Cilfone, J. R. Gulcher, D. A. Lidar, and T. W. Chittenden, Quantum processor-inspired machine learning in the biomedical sciences, arXiv preprint arXiv: 1909.06206, 2019.
61

B. N. Wang, F. Hu, H. N. Yao, and C. Wang, Prime factorization algorithm based on parameter optimization of Ising model, Scientific Reports, vol. 10, p. 7106, 2020.

62

J. M. Liu, Y. H. Lu, R. X. Wang, Z. F. Xu, and X. Li, Simple and efficient combustion method for preparation of high-performance Co3O4 anode materials for lithium-ion batteries, JOM, vol. 72, no. 9, pp. 3296–3302, 2020.

63

Y. W. Zhao, D. J. Peng, J. L. Zhang, and B. Wu, Quantum evolutionary algorithm for capacitated vehicle routing problem, (in Chinese), Systems Engineering-Theory&Practice, vol. 29, no. 2, pp. 159–166, 2009.

64

H. Y. Liu and C. H. Zhou, Application of quantum particle swarm optimization algorithm in transportation problem, (in Chinese), Journal of Intelligent Manufacturing, vol. 27, no. 1, pp. 11–14, 2011.

65
R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Proc. the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
66
J. L. 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. 10, pp. 939–955, 2012.https://doi.org/10.1155/2012/879614
67
L. X. Wang, S. K. Kowk, and W. H. Ip, Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems, Journal of Intelligent Manufacturing, vol. 23, no. 6, pp. 2227–2236, 2012.https://doi.org/10.1007/s10845-011-0568-7
68

W. W. Huang, J. W. Zhang, S. J. Liang, and H. Y. Sun, Backbone network traffic prediction based on modified EEMD and quantum neural network, Wireless Personal Communications:An International Journal, vol. 99, no. 4, pp. 1569–1588, 2018.

69
D. Ventura and S. Kak, Quantum computing and neural information processing, Information Sciences: An International Journal, vol. 128, nos. 3&4, pp. 147–148, 2000.https://doi.org/10.1016/S0020-0255(00)00049-9
70

Q. Q. Zhang, S. F. Liu, D. Q. Gong, H. K. 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.

71
F. Q. Zhang, T. Wu, Y. Wang, R. Xiong, G. Y. Ding, P. Mei, and L. Y. Liu, Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction, IEEE Access, vol. 8, pp. 104555–104564, 2020.https://doi.org/10.1109/ACCESS.2020.2999608
72

J. B. Sheu, A quantum mechanics-based approach to model incident-induced dynamic driver behavior, Physica D Nonlinear Phenomena, vol. 237, no. 13, pp. 1800–1814, 2008.

73

M. Bernas and J. Wisniewska, Quantum road traffic model for ambulance travel time estimation, Journal of Medical Informatics&Technologies, vol. 22, pp. 257–264, 2013.

74

J. F. Dai and Z. Wang, Separated vehicle scheduling optimisation for container trucking transportation based on hybrid quantum evolutionary algorithm, International Journal of Computing Science and Mathematics, vol. 8, no. 5, pp. 405–413, 2017.

75
L. K. Grover, A fast quantum mechanical algorithm for database search, in Proc. 28thACM Symposium on Theory of Computing, Philadelphia, PA, USA, 1996, pp. 212–219.https://doi.org/10.1145/237814.237866
76

P. Campigotto, C. Rudloff, M. Leodolter, and D. Bauer, Personalized and situation-aware multimodal route recommendations: The FAVOUR algorithm, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 92–102, 2017.

77

H. L. Xiao, A. T. Chronopoulos, and Z. S. 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, 2020.

78

A. Che, P. Wu, F. Chu, and M. C. Zhou, Improved quantum-inspired evolutionary algorithm for large-size lane reservation, IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 45, no. 12, pp. 1535–1548, 2015.

79

W. B. Hu, H. Wang, Z. Y. Qiu, C. Nie, and L. P. Yan, A quantum particle swarm optimization driven urban traffic light scheduling model, Neural Computing&Applications, vol. 29, no. 3, pp. 901–911, 2018.

80

X. Q. Huang, J. Chen, H. Yang, Y. J. Cao, W. D. Guan, and B. C. Huang, Economic planning approach for electric vehicle charging stations integrating traffic and power grid constraints, IET Generation,Transmission&Distribution, vol. 12, no. 17, pp. 3925–3934, 2018.

81

A. Syrichas and A. Crispin, Large-scale vehicle routing problems: Quantum annealing, tunings and results, Computers&Operations Research, vol. 87, pp. 52–62, 2017.

82

T. Stollenwerk, B. O’Gorman, D. Venturelli, S. Mandrà, O. Rodionova, H. Ng, B. Sridhar, E. G. Rieffel, and R. Biswas, Quantum annealing applied to de-conflicting optimal trajectories for air traffic management, IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 285–297, 2020.

83
H. Irie, G. Wongpaisarnsin, M. Terabe, A. Miki, and S. Taguchi, Quantum annealing of vehicle routing problem with time, state and capacity, in Proc. 2019 International Workshop on Quantum Technology and Optimization Problems, Munich, Germany, 2019, pp. 145–156.https://doi.org/10.1007/978-3-030-14082-3_13
84
A. Crispin and A. Syrichas, Quantum annealing algorithm for vehicle scheduling, in Proc. 2013 IEEE International Conference on Systems,Man, and Cybernetics, Manchester, UK, 2013, pp. 3523–3528.https://doi.org/10.1109/SMC.2013.601
85
T. Stollenwerk, E. Lobe, and M. Jung, Flight gate assignment with a quantum annealer, in Proc. 2019 International Workshop on Quantum Technology and Optimization Problems, Munich, Germany, 2019, pp. 99–110.https://doi.org/10.1007/978-3-030-14082-3_9
86
H. Hussain, M. B. Javaid, F. S. Khan, A. Dalal, and A. Khalique, Optimal control of traffic signals using quantum annealing, arXiv preprint arXiv: 1912.07134, 2019.https://doi.org/10.1007/s11128-020-02815-1
87

N. Wang, G. G. Guo, B. N. Wang, and C. Wang, Traffic clustering algorithm of urban data brain based on a hybrid-augmented architecture of quantum annealing and brain-inspired cognitive computing, Tsinghua Science and Technology, vol. 25, no. 6, pp. 813–825, 2020.

88
V. N. Smelyanskiy, E. G. Rieffel, S. I. Knysh, C. P. Williams, M. W. Johnson, M. C. Thom, W. G. Macready, and K. L. Pudenz, A near-term quantum computing approach for hard computational problems in space exploration, arXiv preprint arXiv: 1204.2821, 2012.
Intelligent and Converged Networks
Pages 259-276
Cite this article:
Wang S, Pei Z, Wang C, et al. Shaping the future of the application of quantum computing in intelligent transportation system. Intelligent and Converged Networks, 2021, 2(4): 259-276. https://doi.org/10.23919/ICN.2021.0019

1612

Views

847

Downloads

14

Crossref

12

Scopus

Altmetrics

Received: 15 November 2021
Accepted: 16 December 2021
Published: 30 December 2021
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

Return