Research
Funding/Proposal Experience
“ERI: Variational Quantum Algorithm for Power System Simulation”, a two-year project awarded by U.S. NSF in Mar. 2022 ($199,980, the sole principal investigator (PI))
“Advanced Manufacturing and Characterization Research of High Temperature Materials”, a five-year project funded by the U.S. Army Research Laboratory, approved in May 2022 ($2.7M per year, co-PI)
Participated in preparing a proposal for an NSERC/SaskPower Senior Industrial Research Chair in Smart Grid Technologies Grant. The proposal was approved in Feb. 2016 and brought $ 3,500,000 to the University of Saskatchewan
In charge of “Project 2.2: Application of real-time thermal rating in distribution systems” and “Project 3.3: Integrated optimization of the transmission network and microgrid planning”.
Participated in the writing of a proposal for a Key Project of the National Natural Science Foundation of China: “Research on the fundamental theories and methods for the planning and operation of integrated energy networks”, approved in 2014.
In charge of “1.2 Literature Review” and “3.1 Research Method: Multi-objective Stochastic Optimization Methods”.
Research Highlights
[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. We prove that the Variational Quantum Search, with a single layer of Ry gates as its Ansatz, has near-perfect reachability in finding the good element of an arbitrarily large unstructured data set, and its reachability exponentially improves with the number of qubits.
[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 Algortihm, 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. The figure shows the quantum circuit for the Quantum Feasibility Labeling algorithm, where FLQ is short for feasibility label qubit.
[J25] J. P. Zhan. Variational Quantum Search with Exponential Speedup, arXiv:2212.09505, Dec. 2022 [https://arxiv.org/abs/2212.09505]
Highlight: The figure demonstrates that the circuit depth of VQS and Grover’s algorithm increases linearly and exponentially, respectively. That is, VQS achieves an exponential advantage over Grover’s algorithm in terms of circuit depth. We envisage our VQS could exponentially speed up the solutions to many important problems, including the NP-complete problems, which is widely considered impossible.
Research
As a Postdoctoral Fellow at the University of Saskatchewan
Project: Smart Grid Operation and Planning of Power Systems with Compressed Air Energy Storage (Supported by NSERC/SaskPower Senior Industrial Research Chair in Smart Grid Technologies Grant, Canada), 2016 – 2018
Developed an accurate bi-linear cavern model for compressed air energy storage
Integrated compressed air energy storage in smart grid planning and operation
Project: Smart Grid Planning Considering Dynamic Thermal Rating Under Uncertainty (Supported by SaskPower Chair in Power Systems Engineering Grant, Canada, and by NSERC/SaskPower Senior Industrial Research Chair in Smart Grid Technologies Grant, Canada), 2015 – 2018
Proposed a time series model for the dynamic thermal rating of overhead transmission lines
Integrated dynamic thermal rating into the power system transmission expansion planning problem
Generated high-quality wind/load/weather scenarios under uncertainty
As a Postdoctoral Fellow at The Hong Kong Polytechnic University
Project: Smart Grid Planning With Consideration of Load and Wind Farm Uncertainties (Supported by The Hong Kong Polytechnic University, HK), 2014
Investigated the robust/stochastic optimization and Benders decomposition methods for the transmission expansion planning under uncertainty
Implemented scenario generation and reduction methods for uncertain load and wind farm data
As a Visiting Research Assistant at the South China University of Technology
Project: Energy-saving Generation Dispatching Scheme of Guangdong Province (Supported by Guangdong Yudean Group Co. Ltd. and Guangzhou Power Supply Co. Ltd.)
Collected real data from generation units
As a Visiting Research Assistant at the University of Liverpool
Project: Multi-objective optimization for dispatch and control of smart grids, 2012 – 2013
Developed the solution methods for the economic dispatch considering the prohibited operating zones and the valve-point effect of generation units
As a Ph.D. and undergraduate student at the Zhejiang University
Project: Guide for Maintenance Strategies and Techniques of Transmission and Transformation Equipment (Supported by Guangdong Power Grid Co., Ltd.), 2010 – 2010
Conducted research on the reliability-centered maintenance scheduling of generators and transmission lines
Conducted research on the multi-objective generation maintenance scheduling
Project: Research on the Smart Modeling and Optimization Algorithm for Distributed Power System Dynamic Economic Dispatch Considering Wind Farms (Supported by Program for New Century Excellent Talents in University, No. NCET-07-0745), 2009 – 2011
Conducted research on the economic dispatch problems considering wind farms
Conducted research on the solution methods for the dynamic economic dispatch model
Project: Energy Management System in Enterprise with Distributed Energies(Supported by National High Technology Research and Development 863 Program), 2008 – 2011
Learned to develop the software for the project in the co-operative company
Conducted research on the short-term load forecasting
Integrated the load forecasting method into the software for the project