Journal Home > Volume 1 , Issue 2

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.


menu
Abstract
Full text
Outline
About this article

Quantum computing in power systems

Show Author's information Yifan Zhou1Zefan Tang2Nima Nikmehr1Pouya Babahajiani1Fei Feng1Tzu-Chieh Wei3Honghao Zheng4Peng Zhang1,2( )
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Interdisciplinary Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA
CN Yang Institute for Theoretical Physics and Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794, USA
Smart Grid and Technology, Commonwealth Edison, Oakbrook, IL 60523, USA

Abstract

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

Keywords: power system, Quantum computing, variational quantum algorithms, quantum optimization, quantum machine learning, quantum security

References(164)

1
Office of the Federal Chief Sustainability Officer. (2022). Federal Sustainability Plan. Available at https://www.sustainability.gov/federalsustainabilityplan/.
2
New York State. (2022). Climate Leadership and Community Protection Act. Available at https://climate.ny.gov/.
3
Vaahedi, E. (2014). Practical Power System Operation. Hoboken, NJ, USA: John Wiley & Sons.
DOI
4

Kurt, M. N., Yılmaz, Y., Wang, X. D. (2020). Secure distributed dynamic state estimation in wide-area smart grids. IEEE Transactions on Information Forensics and Security, 15: 800–815.

5

Nelson, J. R., Johnson, N. G. (2020). Model predictive control of microgrids for real-time ancillary service market participation. Applied Energy, 269: 114963.

6

Jia, Y. W., Lyu, X., Xie, P., Xu, Z., Chen, M. H. (2020). A novel retrospect-inspired regime for microgrid real-time energy scheduling with heterogeneous sources. IEEE Transactions on Smart Grid, 11: 4614–4625.

7

Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574: 505–510.

8
Roushan, P. (2020). Quantum supremacy: Computational complexity and applications. In: Proceedings of the APS March Meeting 2020, Denver, Colorado, USA.
9
Nielsen, M. A., Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge: Cambridge University Press.
10
Wiseman, H. M., Milburn, G. J. (2009). Quantum Measurement and Control. Cambridge: Cambridge University Press.
DOI
11

Lara, P. D. M., Maldonado-Ruiz, D. A., Díaz, S. D. A., López, L. I. B., Caraguay, Á. L. V. (2019). Trends on computer security: Cryptography, user authentication, denial of service and intrusion detection. arXiv preprint: 1903.08052.

12

Feng, F., Zhou, Y. F., Zhang, P. (2021). Quantum power flow. IEEE Transactions on Power Systems, 36: 3810–3812.

13
Feng, F., Zhang, P., Zhou, Y. F., Tang, Z. F. (2022). Quantum microgrid state estimation. In: Proceedings of the 2022 Power Systems Computation Conference (PSCC), Porto, Portugal.
DOI
14

Zhou, Y. F., Feng, F., Zhang, P. (2021). Quantum electromagnetic transients program. IEEE Transactions on Power Systems, 36: 3813–3816.

15
Zhou, Y. F., Zhang, P., Feng, F. (2022). Noisy-intermediate-scale quantum electromagnetic transients program. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3172655.
DOI
16
Nikmehr, N., Zhang, P. (2022). Quantum distribution system reliability assessment. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (GM), Denver, Colorado.
DOI
17
Nikmehr, N., Zhang, P., Bragin, M. (2022). Quantum distributed unit commitment. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3141794.
DOI
18
Nikmehr, N., Zhang, P., Bragin, M. (2022). Quantum enabled distributed unit commitment. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (GM), Denver, Colorado.
19
Feng, F., Zhang, P., Bragin, M. A., Zhou, Y. F. (2022). Novel resolution of unit commitment problems through quantum surrogate lagrangian relaxation. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3181221.
DOI
20
Zhou, Y. F., Zhang, P. (2022). Noise-resilient quantum machine learning for stability assessment of power systems. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3160384.
DOI
21
Tang, Z. F., Zhang, P., Zhou, Y. F. (2022). Quantum renewable scenario generation. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (GM), Denver, Colorado.
22
Babahajiani, P., Zhang, P. (2022). Quantum distributed microgrid control. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (GM), Denver, Colorado.
23
Babahajiani, P., Zhang, P. (2022). Quantum-secure distributed frequency control. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (GM), Denver, Colorado.
24

Tang, Z. F., Qin, Y. Y., Jiang, Z. M., Krawec, W. O., Zhang, P. (2021). Quantum-secure microgrid. IEEE Transactions on Power Systems, 36: 1250–1263.

25
Tang, Z. F., Qin, Y. Y., Jiang, Z. M., Krawec, W. O., Zhang, P. (2020). Quantum-secure networked microgrids. In: Proceedings of the 2020 IEEE Power & Energy Society General Meeting, Montreal, QC, Canada.
DOI
26

Tang, Z. F., Zhang, P., Krawec, W. O., Jiang, Z. M. (2020). Programmable quantum networked microgrids. IEEE Transactions on Quantum Engineering, 1: 4101013.

27

Tang, Z. F., Zhang, P., Krawec, W. O. (2021). A quantum leap in microgrids security: The prospects of quantum-secure microgrids. IEEE Electrification Magazine, 9: 66–73.

28
Tang, Z. F., Zhang, P., Krawec, W., Wang, L. Z. (2022). Quantum networks for resilient power grids: Theory and simulated evaluation. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3172374.
DOI
29

Jiang, Z. M., Tang, Z. F., Qin, Y. Y., Kang, C. Q., Zhang, P. (2021). Quantum internet for resilient electric grids. International Transactions on Electrical Energy Systems, 31: e12911.

30

Humble, T. S., Thapliyal, H., Muñoz-Coreas, E., Mohiyaddin, F. A., Bennink, R. S. (2019). Quantum computing circuits and devices. IEEE Design & Test, 36: 69–94.

31

Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2: 79.

32

Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., et al. (2021). Variational quantum algorithms. Nature Reviews Physics, 3: 625–644.

33

García, D. P., Cruz-Benito, J., García-Peñalvo, F. J. (2022). Systematic literature review: Quantum machine learning and its applications. arXiv preprint: 2201.04093.

34

Preskill, J. (2012). Quantum computing and the entanglement frontier. arXiv preprint: 1203.5813.

35

Harrow, A. W., Montanaro, A. (2017). Quantum computational supremacy. Nature, 549: 203–209.

36

Feynman, R. P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21: 467–488.

37

Lloyd, S. (1996). Universal quantum simulators. Science, 273: 1073–1078.

38
Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, NM, USA.
39

Freedman, M. H., Larsen, M., Wang, Z. H. (2002). A modular functor which is universal for quantum computation. Communications in Mathematical Physics, 227: 605–622.

40

Harrow, A. W., Hassidim, A., Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103: 150502.

41
Aaronson, S., Arkhipov, A. (2011). The computational complexity of linear optics. In: Proceedings of the Forty-third Annual ACM Symposium on Theory of Computing, New York, NY, USA.
DOI
42

Wang, H., Qin, J., Ding, X., Chen, M. C., Chen, S., You, X., He, Y. M., Jiang, X., You, L., Wang, Z., et al. (2019). Boson sampling with 20 input photons and a 60-mode interferometer in a 1014-dimensional Hilbert space. Physical Review Letters, 123: 250503.

43
IBM. (2016). The largest, highest performing fleet of quantum systems in the world. Available at https://www.ibm.com/quantum/systems. Accessed 01 Feb, 2022.
44
IBM. (2021). IBM Q Experience. Available at https://quantum-computing.ibm.com/.
45
Anis, M. S., Abraham, H., AduOffei, R. A., Agliardi, G., Aharoni, M., Akhalwaya, I. Y., Aleksandrowicz, G., Alexander T., Amy M., Anagolum S., et al. (2021). Qiskit: An open-source framework for quantum computing, https://doi.org/10.5281/zenodo.2573505.
46
Gambetta, J. (2020). IBM’s roadmap for scaling quantum technology. Available at https://research.ibm.com/blog/ibm-quantum-roadmap.
47
Google. Google Quantum AI. Available at https://quantumai.google/.
48
Google. (2018). Cirq: An open source framework for programming quantum computers. Available at https://quantumai.google/cirq.
49
Google. (2017). OpenFermion: The open source chemistry package for quantum computers. Available at https://quantumai.google/openfermion.
50
Google. (2020). TensorFlow Quantum: A library for hybrid quantum classical machine learning. Available at https://www.tensorflow.org/quantum.
51

Pednault, E., Gunnels, J. A., Nannicini, G., Horesh, L., Wisnieff, R. (2019). Leveraging secondary storage to simulate deep 54-qubit sycamore circuits. arXiv preprint: 1910.09534.

52

Huang, C. , Zhang, F., Newman, M., Cai, J. J., Gao, X., Tian, Z. X., Wu, J. Y., Xu, H. H., Yu, H. J., Yuan, B., et al. (2020). Classical simulation of quantum supremacy circuits. arXiv preprint: 2005.06787.

53

Pan, F., Zhang, P. (2021). Simulating the Sycamore quantum supremacy circuits. arXiv preprint: 2103.03074.

54
Xanadu. Xanadu Quantum Cloud. Available at https://www.xanadu.ai/.
55
Xanadu. Strawberry Fields: A cross-platform python library for simulating and executing programs on quantum photonic hardware. Available at https://strawberryfields.ai/.
56
Xanadu. PennyLane: A cross-platform python library for differentiable programming of quantum computers. Available at https://pennylane.ai/.
57
D-Wave. Unlock the power of practical quantum computing today. Available at https://www.dwavesys.com/.
58
McGeoch, C., Farré, P. (2020). The D-wave advantage system: An overview. Technical report. D-Wave Systems Inc., Burnaby, BC, Canada.
59
Alibaba Cloud. Alibaba Cloud and CAS launch one of the world’s most powerful public quantum computing services. Available at https://www.alibabacloud.com/press-room/alibaba-cloud-and-cas-launch-one-of-the-worlds-most.
60

Ball, P. (2020). Physicists in China challenge Google’s ‘quantum advantage’. Nature, 588: 380.

61
Shaw, D. (2022). Quantum hardware outlook 2022. Technical report. Available at https://www.factbasedinsight.com/quantum-hardware-outlook-2022/.
62
Feng, F., Zhang, P., Zhou, Y. F., Wang, L. Z. (2022). Distributed networked microgrids power flow. IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2022.3175933.
DOI
63

Zhou, Y. F., Zhang, P. (2020). Reachable power flow. IEEE Transactions on Power Systems, 35: 3290–3293.

64

Zhou, Y. F., Zhang, P. (2021). Reachable power flow: Theory to practice. IEEE Transactions on Power Systems, 36: 2532–2541.

65

Feng, F., Zhang, P. (2020). Enhanced microgrid power flow incorporating hierarchical control. IEEE Transactions on Power Systems, 35: 2463–2466.

66

Feng, F., Zhang, P. (2020). Implicit Zbus Gauss algorithm revisited. IEEE Transactions on Power Systems, 35: 4108–4111.

67

Stott, B., Alsac, O. (1974). Fast decoupled load flow. IEEE Transactions on Power Apparatus and Systems, PAS-93: 859–869.

68

Feng, F., Zhang, P., Zhou, Y. F. (2022). Authentic microgrid state estimation. IEEE Transactions on Power Systems, 37: 1657–1660.

69

Larson, R. E., Tinney, W. F., Peschon, J. (1970). State estimation in power systems part I: Theory and feasibility. IEEE Transactions on Power Apparatus and Systems, PAS-89: 345–352.

70

Dervovic, D., Herbster, M., Mountney, P., Severini, S., Usher, N., Wossnig, L. (2018). Quantum linear systems algorithms: A primer. arXiv preprint: 1802.08227.

71

Bravo-Prieto, C., LaRose, R., Cerezo, M., Subasi, Y., Cincio, L., Coles, P. J. (2019). Variational quantum linear solver. arXiv preprint: 1909.05820.

72
NERC. (2021). Odessa disturbance: Texas events: May 9, 2021 and June 26, 2021. Joint NERC and Texas RE Staff Report. North American Electric Reliability Corporation (NERC), Atlanta, GA, USA.
73

Zhou, Y. F., Zhang, P., Yue, M. (2021). Reachable dynamics of networked microgrids with large disturbances. IEEE Transactions on Power Systems, 36: 2416–2427.

74

Zhou, Y. F., Zhang, P. (2022). Neuro-reachability of networked microgrids. IEEE Transactions on Power Systems, 37: 142–152.

75
Zhou, Y. F., Zhang, P., Yue, M. (2020). An ODE-enabled distributed transient stability analysis for networked microgrids. In: Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada.
DOI
76
Dommel, H. W. (1996). EMTP Theory Book. Vancouver, Canada: Microtran Power System Analysis Corporation.
77
Zhou, Y. F., Zhang, P. (2022). Neural electromagnetic transients program. In: Proceedings of the 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, Colorado.
78
Hidary, J. D. (2019). Quantum Computing: An Applied Approach. Cham, Switzerland: Springer.
DOI
79

Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K. (2018). Quantum circuit learning. Physical Review A, 98: 032309.

80

Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., Killoran, N. (2019). Evaluating analytic gradients on quantum hardware. Physical Review A, 99: 032331.

81

Zhang, P., Marti, J. R., Dommel, H. W. (2007). Synchronous machine modeling based on shifted frequency analysis. IEEE Transactions on Power Systems, 22: 1139–1147.

82

Zhang, P., Marti, J. R., Dommel, H. W. (2009). Induction machine modeling based on shifted frequency analysis. IEEE Transactions on Power Systems, 24: 157–164.

83

Zhang, P., Marti, J. R., Dommel, H. W. (2010). Shifted-frequency analysis for EMTP simulation of power-system dynamics. IEEE Transactions on Circuits and Systems I: Regular Papers, 57: 2564–2574.

84

Jooshaki, M., Abbaspour, A., Fotuhi-Firuzabad, M., Muñoz-Delgado, G., Contreras, J., Lehtonen, M., Arroyo, J. M. (2022). An enhanced MILP model for multistage reliability-constrained distribution network expansion planning. IEEE Transactions on Power Systems, 37: 118–131.

85

Bie, Z. H., Zhang, P., Li, G. F., Hua, B. W., Meehan, M., Wang, X. F. (2012). Reliability evaluation of active distribution systems including microgrids. IEEE Transactions on Power Systems, 27: 2342–2350.

86
Allan, R. N. (2013). Reliability Evaluation of Power Systems. New York: Springer Science & Business Media.
87

Montanaro, A. (2015). Quantum speedup of Monte Carlo methods. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 471: 20150301.

88
Rubinstein, R. Y., Kroese, D. P. (2016). Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc.
DOI
89

Heinrich, S. (2002). Quantum summation with an application to integration. Journal of Complexity, 18: 1–50.

90

Brassard, G., Hoyer, P., Mosca, M., Tapp, A. (2002). Quantum amplitude amplification and estimation. Contemporary Mathematics, 305: 53–74.

91

Wocjan, P., Chiang, C. F., Nagaj, D., Abeyesinghe, A. (2009). Quantum algorithm for approximating partition functions. Physical Review A, 80: 022340.

92

Grover, L. K. (1997). Quantum mechanics helps in searching for a needle in a haystack. Physical Review Letters, 79: 325–328.

93

Yan, B., Luh, P. B., Warner, G., Zhang, P. (2017). Operation and design optimization of microgrids with renewables. IEEE Transactions on Automation Science and Engineering, 14: 573–585.

94

Zhou, Y. F., Hu, W., Min, Y., Dai, Y. H. (2019). Integrated power and heat dispatch considering available reserve of combined heat and power units. IEEE Transactions on Sustainable Energy, 10: 1300–1310.

95

Cao, Y. J., Tang, S. W., Li, C. B., Zhang, P., Tan, Y., Zhang, Z. K., Li, J. X. (2012). An optimized EV charging model considering TOU price and SOC curve. IEEE Transactions on Smart Grid, 3: 388–393.

96

Wan, W. F., Bragin, M. A., Yan, B., Qin, Y. Y., Philhower, J., Zhang, P., Luh, P. B. (2020). Distributed and asynchronous active fault management for networked microgrids. IEEE Transactions on Power Systems, 35: 3857–3868.

97

Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint: 1411.4028.

98

Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., et al. (2018). Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology, 3: 030503.

99

Tanahashi, K., Takayanagi, S., Motohashi, T., Tanaka, S. (2019). Application of Ising machines and a software development for Ising machines. Journal of the Physical Society of Japan, 88: 061010.

100
Kundur, P., Balu, N. J., Lauby, M. G. (1994). Power System Stability and Control. New York: McGraw-hill.
101

Eskandarpour, R., Khodaei, A. (2018). Leveraging accuracy-uncertainty tradeoff in SVM to achieve highly accurate outage predictions. IEEE Transactions on Power Systems, 33: 1139–1141.

102

Yadav, R., Raj, S., Pradhan, A. K. (2019). Real-time event classification in power system with renewables using kernel density estimation and deep neural network. IEEE Transactions on Smart Grid, 10: 6849–6859.

103

Huang, Q. H., Huang, R. K., Hao, W. T., Tan, J., Fan, R., Huang, Z. Y. (2020). Adaptive power system emergency control using deep reinforcement learning. IEEE Transactions on Smart Grid, 11: 1171–1182.

104
Schuld, M., Petruccione, F. (2018). Supervised Learning with Quantum Computers. Berlin: Springer.
DOI
105
Wittek, P. (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Cambridge, Massachusetts, UK: Academic Press.
DOI
106

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S. (2017). Quantum machine learning. Nature, 549: 195–202.

107

Lloyd, S., Mohseni, M., Rebentrost, P. (2014). Quantum principal component analysis. Nature Physics, 10: 631–633.

108

Li, Z. K., Chai, Z. H., Guo, Y. H., Ji, W. T., Wang, M. Q., Shi, F. Z., Wang, Y., Lloyd, S., Du, J. F. (2021). Resonant quantum principal component analysis. Science Advances, 7: eabg2589.

109

Schuld, M., Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122: 040504.

110

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: 209–212.

111

Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113: 130503.

112
Kerenidis, I., Landman, J., Luongo, A., Prakash, A. (2019). q-means: A quantum algorithm for unsupervised machine learning. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.
113

Dallaire-Demers, P. L., Killoran, N. (2018). Quantum generative adversarial networks. Physical Review A, 98: 012324.

114

Niu, M. Y., Zlokapa, A., Broughton, M., Boixo, S., Mohseni, M., Smelyanskyi, V., Neven, H. (2021). Entangling quantum generative adversarial networks. arXiv preprint: 2105.00080.

115

Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B., Melko, R. (2018). Quantum Boltzmann machine. Physical Review X, 8: 021050.

116

Jerbi, S., Trenkwalder, L. M., Poulsen Nautrup, H., Briegel, H. J., Dunjko, V. (2021). Quantum enhancements for deep reinforcement learning in large spaces. PRX Quantum, 2: 010328.

117

Beer, K., Bondarenko, D., Farrelly, T., Osborne, T. J., Salzmann, R., Scheiermann, D., Wolf, R. (2020). Training deep quantum neural networks. Nature Communications, 11: 808.

118

Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., Killoran, N. (2020). Quantum embeddings for machine learning. arXiv preprint: 2001.03622.

119

Yu, J. J. Q., Hill, D. J., Lam, A. Y. S., Gu, J. T., Li, V. O. K. (2018). Intelligent time-adaptive transient stability assessment system. IEEE Transactions on Power Systems, 33: 1049–1058.

120
Zheng, L., Hu, W., Zhou, Y. F., Min, Y., Xu, X. L., Wang, C. M., Yu, R. (2017). Deep belief network based nonlinear representation learning for transient stability assessment. In: Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA.
DOI
121

Sim, S., Johnson, P. D., Aspuru-Guzik, A. (2019). Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2: 1900070.

122

Rasmussen, S. E., Loft, N. J. S., Bækkegaard, T., Kues, M., Zinner, N. T. (2020). Reducing the amount of single-qubit rotations in VQE and related algorithms. Advanced Quantum Technologies, 3: 2000063.

123

Kim, J., Kim, J., Rosa, D. (2021). Universal effectiveness of high-depth circuits in variational eigenproblems. Physical Review Research, 3: 023203.

124

Chen, S. Y. C., Wei, T. C., Zhang, C., Yu, H. W., Yoo, S. (2021). Hybrid quantum-classical graph convolutional network. arXiv preprint: 2101.06189.

125

Wan, W. F., Zhang, P., Bragin, M. A., Luh, P. B. (2022). Cooperative fault management for resilient integration of renewable energy. Electric Power Systems Research, 211: 108147.

126

Yu, F. R., Zhang, P., Xiao, W. D., Choudhury, P. (2011). Communication systems for grid integration of renewable energy resources. IEEE Network, 25: 22–29.

127

Babahajiani, P., Wang, L. Z., Liu, J., Zhang, P. (2021). Push-sum-enabled resilient microgrid control. IEEE Transactions on Smart Grid, 12: 3661–3664.

128

Molzahn, D. K., Dörfler, F., Sandberg, H., Low, S. H., Chakrabarti, S., Baldick, R., Lavaei, J. (2017). A survey of distributed optimization and control algorithms for electric power systems. IEEE Transactions on Smart Grid, 8: 2941–2962.

129

Nguyen, D. H., Khazaei, J. (2021). Unified distributed control of battery storage with various primary control in power systems. IEEE Transactions on Sustainable Energy, 12: 2332–2341.

130

Chen, Y. L., Qi, D. L., Dong, H. N., Li, C. Y., Li, Z. M., Zhang, J. L. (2021). A FDI attack-resilient distributed secondary control strategy for islanded microgrids. IEEE Transactions on Smart Grid, 12: 1929–1938.

131

Wright, K., Beck, K. M., Debnath, S., Amini, J. M., Nam, Y., Grzesiak, N., Chen, J. S., Pisenti, N. C., Chmielewski, M., Collins, C., et al. (2019). Benchmarking an 11-qubit quantum computer. Nature Communications, 10: 5464.

132

Popkin, G. (2021). The Internet goes quantum. Science, 372: 1026–1029.

133

Kimble, H. J. (2008). The quantum internet. Nature, 453: 1023–1030.

134

Yu, Y., Ma, F., Luo, X. Y., Jing, B., Sun, P. F., Fang, R. Z., Yang, C. W., Liu, H., Zheng, M. Y., Xie, X. P., et al. (2020). Entanglement of two quantum memories via fibres over dozens of kilometres. Nature, 578: 240–245.

135

Wehner, S., Elkouss, D., Hanson, R. (2018). Quantum internet: A vision for the road ahead. Science, 362: eaam9288.

136

Castelvecchi, D. (2021). Quantum network is step towards ultrasecure Internet. Nature, 590: 540–541.

137

Azuma, K., Mizutani, A., Lo, H. K. (2016). Fundamental rate-loss trade-off for the quantum internet. Nature Communications, 7: 13523.

138

Pirandola, S., Braunstein, S. L. (2016). Physics: Unite to build a quantum internet. Nature, 532: 169–171.

139

Castelvecchi, D. (2018). The quantum internet has arrived (and it hasn’t). Nature, 554: 289–292.

140
Dowling, J. P. (2020). Schrödinger’s Web: Race to Build the Quantum Internet. Boca Raton, Florida, USA: CRC Press.
DOI
141

Qi, R. Y., Sun, Z., Lin, Z. S., Niu, P. H., Hao, W. T., Song, L. Y., Huang, Q., Gao, J. C., Yin, L. G., Long, G. L. (2019). Implementation and security analysis of practical quantum secure direct communication. Light: Science & Applications, 8: 22.

142

Awschalom, D., Berggren, K. K., Bernien, H., Bhave, S., Carr, L. D., Davids, P., Economou, S. E., Englund, D., Faraon, A., Fejer, M., et al. (2021). Development of quantum interconnects (QuICs) for next-generation information technologies. PRX Quantum, 2: 017002.

143

Tang, Z. F., Zhang, P., Muto, K., Sawasawa, M., Simonelli, M., Gutierrez, C., Yang, J., Astitha, M., Ferrante, D. A., Debs, J. N., et al. (2018). Extreme photovoltaic power analytics for electric utilities. IEEE Transactions on Sustainable Energy, 11: 93–106.

144
Zhang, P., Tang, Z. F., Yang, J., Muto, K., Liu, X. B., Astitha, M., Debs, J. N., Ferrante, D. A., Marcaurele, D., Hazlewood, I. M., et al. (2018). PV extreme capacity factor analysis. In: Proceedings of the 2018 IEEE Power & Energy Society General Meeting, Portland, OR, USA.
DOI
145

Jiao, J. Y., Tang, Z. F., Zhang, P., Yue, M., Yan, J. (2022). Cyberattack-resilient load forecasting with adaptive robust regression. International Journal of Forecasting, 38: 910–919.

146
Jiao, J. Y., Tang, Z. F., Zhang, P., Yue, M., Chen, C., Yan, J. (2019). Ensuring cyberattack-resilient load forecasting with a robust statistical method. In: Proceedings of the 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA.
DOI
147
Tang, Z. F., Jiao, J. Y., Zhang, P., Yue, M., Chen, C., Yan, J. (2019). Enabling cyberattack-resilient load forecasting through adversarial machine learning. In: Proceedings of the 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA.
DOI
148

Etingov, D. A., Zhang, P., Tang, Z. F., Zhou, Y. F. (2022). AI-enabled traveling wave protection for microgrids. Electric Power Systems Research, 210: 108078.

149

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35: 53–65.

150

Grant, E., Benedetti, M., Cao, S. X., Hallam, A., Lockhart, J., Stojevic, V., Green, A. G., Severini, S. (2018). Hierarchical quantum classifiers. Npj Quantum Information, 4: 65.

151
Bresson, E., Chevassut, O., Pointcheval, D. (2002). Dynamic group Diffie-Hellman key exchange under standard assumptions. In: Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, Amsterdam, The Netherlands.
DOI
152
Mollin, R. A. (2002). RSA and Public-Key Cryptography. New York: Chapman and Hall/CRC.
DOI
153

Ekert, A., Jozsa, R. (1996). Quantum computation and Shor’s factoring algorithm. Reviews of Modern Physics, 68: 733–753.

154

Curty, M., Lo, H. K. (2019). Foiling covert channels and malicious classical post-processing units in quantum key distribution. Npj Quantum Information, 5: 14.

155

Sasaki, M., Fujiwara, M., Ishizuka, H., Klaus, W., Wakui, K., Takeoka, M., Miki, S., Yamashita, T., Wang, Z., Tanaka, A., et al. (2011). Field test of quantum key distribution in the Tokyo QKD Network. Optics Express, 19: 10387–10409.

156

Rubin, F. (1996). One-time pad cryptography. Cryptologia, 20: 359–364.

157

Heron, S. (2009). Advanced encryption standard (AES). Network Security, 2009: 8–12.

158

Wang, L. Z., Qin, Y. Y., Tang, Z. F., Zhang, P. (2020). Software-defined microgrid control: The genesis of decoupled cyber-physical microgrids. IEEE Open Access Journal of Power and Energy, 7: 173–182.

159

Wang, J. W., Qin, Y. Y., Tang, Z. F., Zhang, P. (2021). Software-defined cyber–energy secure underwater wireless power transfer. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2: 21–31.

160

Jiang, Z. M., Tang, Z. F., Zhang, P., Qin, Y. Y. (2021). Programmable adaptive security scanning for networked microgrids. Engineering, 7: 1087–1100.

161

Ekert, A. K. (1991). Quantum cryptography based on Bell’s theorem. Physical Review Letters, 67: 661–663.

162

Bartlett, B. (2018). A distributed simulation framework for quantum networks and channels. arXiv preprint: 1808.07047.

163
National Science Foundation. (2017). Quantum leap. Available at https://www.nsf.gov/news/special_reports/big_ideas/quantum.jsp.
164
Zheng, H. H., Khodaei, A., Eskandarpour, R., Paaso, A. (2021). A quantum leap is coming: Ones, zeros and everything in between. Available at https://www.tdworld.com/home/article/21151432/a-quantum-leap-is-coming-ones-zeros-and-everything-in-between.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 03 June 2022
Revised: 10 June 2022
Accepted: 21 June 2022
Published: 15 July 2022
Issue date: June 2022

Copyright

© The author(s) 2022

Acknowledgements

Acknowledgements

This work was supported in part by the Advanced Grid Modeling Program under U.S. Department of Energy’s Office of Electricity under Agreement No. 37533 (P.Z.), in part by Stony Brook University’s Office of the Vice President for Research through a Quantum Information Science and Technology Seed Grant (P.Z.), and in part by the National Science Foundation under Grant No. PHY 1915165 (T.-C.W.). We would like to acknowledge the Brookhaven National Laboratory operated IBM-Q Hub. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility supported under Contract DE-AC05-00OR22725.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return