Publications
Published Journal/Conference Papers and Preprints (Quantum Computing Area)
[J29] J. P. Zhan. Quantum Multiplier Based on Exponent Adder, arXiv: 2309.10204, Sep. 2023 [https://arxiv.org/abs/2309.10204]
Highlight: The Quantum Multiplier Based on Exponent Adder (QMbead) requires log2(n) qubits to multiply two n-bit integer numbers while most existing quantum multipliers need O(n) qubits. It has been implemented on quantum simulators to compute products with a bit length of up to 273 bits using only 17 qubits.
[C9] M. Soltaninia and J. P. Zhan. Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study, 27th Annual IEEE High Performance Extreme Computing Conference (HPEC 2023), 25-29 Sep. 2023. [https://arxiv.org/abs/2309.05924]
[J28] I. K. Tutul, S. Karimi, and J. P. Zhan. Shallow Depth Factoring Based on Quantum Feasibility Labeling and Variational Quantum Search, arXiv:2305.19542, May 2023 [https://arxiv.org/abs/2305.19542]
Highlight: We develop a shallow-depth quantum algorithm to tackle the integer factorization problem, targeting to break cryptography such as RSA public-key encryption.
[J27] J. P. Zhan. Near-perfect Reachability of Variational Quantum Search with Depth-1 Ansatz, arXiv:2301.13224, Jan. 2023 [https://arxiv.org/abs/2301.13224]
Highlight: We show that the exponentially deep circuit required by Grover’s algorithm can be replaced by a multi-controlled NOT gate together with a single layer of Ry gates.
[J26] J. P. Zhan. Quantum Feasibility Labeling for NP-complete Vertex Coloring Problem, arXiv:2301.01589, Jan. 2023 [https://arxiv.org/abs/2301.01589]
Highlight: The Quantum Feasibility Labeling algorithm, together with our Variational Quantum Search (VQS) algorithm, could be the first algorithm to solve an NP-complete problem in polynomial time, provided that the VQS is proved to be efficient for any number of qubits.
[J25] J. P. Zhan. Variational Quantum Search with Shallow Depth for Unstructured Database Search, arXiv:2212.09505, Dec. 2022 [https://arxiv.org/abs/2212.09505]
Highlight: We show that a depth-10 Ansatz can amplify the total probability of k (k≥1) good elements, out of 2n elements represented by n+1 qubits, from k/2n to nearly 1, as verified for n up to 26, and that the maximum depth of quantum circuits in the VQS increases linearly with the number of qubits. We demonstrate that a depth-56 circuit in VQS can replace a depth-270,989 circuit in Grover’s algorithm, and thus VQS is more suitable for NISQ computers.
[J24] Y. L. Liao, and J. P. Zhan. Expressibility-Enhancing Strategies for Quantum Neural Networks, arXiv:2211.12670, Nov. 2022. [https://arxiv.org/abs/2211.12670] (Webex link of my presentation for IEEE PES University Webinar in March 2023)
Highlight: We propose four strategies to improve the performance of quantum neural networks in fitting given functions and reveal that quantum neural networks have an exponential advantage over classical neural networks in terms of expressibility.
Published Journal Papers and Preprints (Power and Energy Area)
[J23] J. J. Yang, Z. Y. Dong, F. S. Wen, Q. X. Chen, F. J. Luo, W. J. Liu, and J. P. Zhan. A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market, IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 4056-4069, 2020
[J22] J. P. Zhan, W. J. Liu, C. Y. Chung, and J. J. Yang, Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration, IEEE Transactions on Smart Grid, accepted in February 2020 (impact factor: 10.486) (Matlab code is available for download at https://github.com/zhanjunpeng/SOE)
[J21] W. J. Liu, J. P. Zhan, C. Y. Chung and L. Sun. Availability Assessment Based Case-Sensitive Power System Restoration Strategy, IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1432-1445, 2020.
[J20] J. P. Zhan, O. A. Ansari, W. J. Liu, and C. Y. Chung. An Accurate Bilinear Cavern Model for Compressed Air Energy Storage. Applied Energy, vol. 242, pp. 752-768, 2019. (Youtube link of my presentation at MIT in 2019)
[J19] W. J. Liu, J. P. Zhan, and C. Y. Chung. A Novel Transactive Energy Control Mechanism for Collaborative Networked Microgrids. IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 2048-2060, 2019.
[J18] W. J. Liu, J. P. Zhan, and C. Y. Chung. Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables. IEEE Transactions on Sustainable Energy, vol. 10, no. 4, pp. 1927-1938, 2019.
[J17] J. P. Zhan, W. J. Liu, and C. Y. Chung. Stochastic Transmission Expansion Planning Considering Uncertain Dynamic Thermal Rating of Overhead Lines. IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 432-443, 2019.
[J16] Y. F. Wen, J. P. Zhan, C. Y. Chung, and W. Y. Li. Frequency Stability Enhancement of Integrated AC/VSC-MTDC Systems with Massive Infeed of Offshore Wind Generation. IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 5135-5146, 2018.
[J15] A. Zare, C. Y. Chung, and J. P. Zhan. A Distributionally Robust Chance-Constrained MILP Model for Multistage Distribution System Planning with Uncertain Renewables and Loads, IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 5248-5262, 2018.
[J14] J. P. Zhan, Y. F. Wen, O. S. Ansari, C. Y. Chung. Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment, arXiv:1709.08275, 2017.
[J13] J. P. Zhan, O. S. Ansari, C. Y. Chung. Compressed air energy storage-Part I: an accurate bi-linear cavern model, arXiv:1709.08272v2, 2017.
[J12] J. P. Zhan, C. Y. Chung, and A. Zare. A Fast Solution Method for Stochastic Transmission Expansion Planning. IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4684-4695, 2017.
[J11] J. P. Zhan, C. Y. Chung, and E. Demeter. Time Series Modelling for Dynamic Thermal Rating of Overhead Lines. IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 2172-2182, 2017.
[J10] J. P. Zhan, Q. H. Wu, C. X. Guo, and X. X. Zhou. Economic Dispatch With Non-convex Objectives–Part II: Dimensional Steepest Decline Method. IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 722-733, 2015.
[J9] J. P. Zhan, Q. H. Wu, C. X. Guo, and X. X. Zhou. Economic Dispatch With Non-convex Objectives–Part I: Local Minimum Analysis. IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 710-721, 2015.
[J8] J. P. Zhan, Q. H. Wu, C. X. Guo, and X. X. Zhou. Fast λ-iteration Method for Economic Dispatch With Prohibited Operating Zones. IEEE Transactions on Power Systems (Power Engineering Letters), vol. 29, no. 2, pp. 990-991, 2014.
[J7] J. P. Zhan, C. X. Guo, Q. H. Wu, and L. L. Zhang. Generation Maintenance Scheduling Based on Multiple Objectives and Their Relationship Analysis. Journal of Zhejiang University Science C (Computers & Electronics), vol. 15, no. 11, pp. 1035-1047, 2014.
[J6] Y. Z. Li, Q. H. Wu, M. S. Li, and J. P. Zhan. Mean-Variance Model for Power System Economic Dispatch with Wind Power Integrated. Energy, vol. 72, pp. 510-520, 2014.
[J5] L. L. Zhang, T. Y. Ji, M. S. Li, Q. H. Wu, L. Jiang, and J. P. Zhan. Morphology Singular Entropy Based Phase Selector Using Short Data Window for Transmission Lines. IEEE Transactions on Power Delivery, vol. 29, no. 5, pp. 2162-2171, 2014.
[J4] C. X. Guo, J. P. Zhan, and Q. H. Wu. Dynamic Economic Emission Dispatch Based on Group Search Optimizer With Multiple Producers. Electric Power Systems Research, vol. 86, pp.8-16, 2012.
[J3] C. X. Guo, Y.H. Bai, X. Zheng, J. P. Zhan, and Q. H. Wu. Optimal Generation Dispatch With Renewable Energy Embedded Using Multiple Objectives. International Journal of Electrical Power and Energy Systems, vol. 42, no. 1, pp. 440-447, 2012.
[J2] J. P. Zhan, C. X. Guo, Q. H. Wu, and B. J. Wen. Fast Group Search Optimizer and its Application on the Economic Dispatch of Power Systems. Proceedings of the CSEE, Supplement, vol. 38, pp. 1-6, 2012. (In Chinese)
[J1] Y. J. Yin, M. Wang, J. J. Zhang, P. Yuan, J. P. Zhan, C. X. Guo. An Autonomic Kernel Optimization Method to Diagnose Transformer Faults by Multi-Kernel Learning Support Vector Classifier Based on Binary Particle Swarm Optimization, Power System Technology, vol. 36, no. 7, pp. 249-254, 2012. (In Chinese)
Peer-Reviewed Conference Papers (Power and Energy Area)
[C8] S. Q. Zhang, A. Yogarathinam, J. P. Zhan, M. Yue, and G. Lin “A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid”, submitted to 2020 IEEE Power & Energy Society General Meeting (IEEE PES GM 2020), Montreal, Canada.
[C7] J. P. Zhan, M. Yue, and L. Fan, “Reliability-Based Stochastic Transmission Expansion Planning Considering Uncertainties of Dynamic Thermal Rating and Wind Power”, 2019 IEEE Power & Energy Society General Meeting (IEEE PES GM 2019), Atlanta, GA, USA.
[C6] J. P. Zhan, Q. H. Wu, C. X. Guo, L. L. Zhang, and M. Bazargan. Impacts of Wind Power Penetration on Combined Economic and Emission Dispatch. The 4th European Innovative Smart Grid Technologies (IEEE PES ISGT Europe 2013) Conference, Copenhagen, Denmark.
[C5] J. P. Zhan, Q. H. Wu, C. X. Guo, J. H. Zheng, L. L. Zhang, and M. Bazargan. Impacts of Wind Power Penetration on Risk Constrained Economic Dispatch. 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC 2013), Kowloon, Hong Kong.
[C4] J. P. Zhan, Y. J. Yin, C. X. Guo, and Q. H. Wu. Integrated Maintenance Scheduling of Generators and Transmission Lines Based on Fast Group Searching Optimizer. 2011 IEEE Power & Energy Society General Meeting (IEEE PES GM 2011), Detroit, Michigan, USA.
[C3] X. D. Chen, J. P. Zhan, Q. H. Wu, and C. X. Guo. Multi-objective Optimization of Generation Maintenance Scheduling. 2014 IEEE Power & Energy Society General Meeting (IEEE PES GM 2014) (Best Conference Paper)
[C2] Y. J. Yin, J. P. Zhan, C. X. Guo, and Q. H. Wu. Multi-Kernel Support Vector Classifier for Fault Diagnosis of Transformers. 2011 IEEE Power & Energy Society General Meeting (IEEE PES GM 2011), Detroit, Michigan, USA.
[C1] L. L. Zhang, T. Y. Ji, M. S. Li, Q. H. Wu, L. Jiang, and J. P. Zhan. Disturbance Identification Based on Mathematical Morphology and Radial Coordinate Visualization. The 4th European Innovative Smart Grid Technologies (IEEE PES ISGT Europe 2013) Conference, Denmark.
Patents
[P6] J. P. Zhan, C. X. Guo, and G. Huang. Short-term Power Load Forecast Method Based on Fast Fuzzy Rough Set. Sep. 2014, China, Application No.: CN201410443464.X (Issued)
[P5] J. P. Zhan, C. X. Guo, and Z. Li. Multi-objective Optimization Method for Generation Maintenance Scheduling in Power System in a Power Market Environment. Sep. 2014, China, Application No.: CN201410442784.3 (Issued)
[P4] Q. H. Wu, J. P. Zhan, and X. X. Zhou. Power System Dispatch Method Based on Combined Multi-objective λ-iteration and Newton Method. Sep. 2013, China, Application No.: CN201310422944.3 (Issued)
[P3] Q. H. Wu, J. P. Zhan, and X. X. Zhou. Power System Dispatch Method Considering Generation Units’ Prohibited Operation Zones Based on Extended λ-iteration Method. Sep. 2013, China, Application No.: CN201310422897.2 (Issued)
[P2] Q. H. Wu and J. P. Zhan. Dimensional Steepest Descent Method for Power Grid Economic Dispatch Problem Considering Valve-point Effect. Oct. 2013, China, Application No.: CN201310482401.0 (Issued)
[P1] Q. H. Wu and J. P. Zhan. Local Minimum Solution Determination and Global Minimum Searching Method for Power Grid Economic Dispatch. Oct. 2013, China, Application No.: CN201310482363.9 (Issued)