As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and soluti...As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.展开更多
Buildings use a large amount of energy in the United States.It is important to optimally manage and coordinate the resources across building and power distribution networks to improve overall efficiency.Optimizing the...Buildings use a large amount of energy in the United States.It is important to optimally manage and coordinate the resources across building and power distribution networks to improve overall efficiency.Optimizing the power grid with discrete variables was very challenging for traditional computers and algorithms,as it is an NP-hard problem.In this study,we developed a new optimization solution based on quantum computing for BTG integration.We first used MPC for building loads connected with a commercial distribution grid for cost reduction.Then we used discretization and Benders Decomposition methods to reformulate the problem and decompose the continuous and discrete variables,respectively.We used D-Wave quantum computer to solve dual problems and used conventional algorithm for primal problems.We applied the proposed method to an IEEE 9-bus network with 3 commercial buildings and over 300 residential buildings to evaluate the feasibility and effectiveness.Compared with traditional optimization methods,we obtained similar solutions with some fluctuations and improved computational speed from hours to seconds.The time of quantum computing was greatly reduced to less than 1% of traditional optimization algorithm and software such as MATLAB.Quantum computing has proved the potential to solve large-scale discrete optimization problems for urban energy systems.展开更多
In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot locat...In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.展开更多
The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized ...The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing,fabrication,and characterization.This review introduces a comprehensive methodology for materials design using cutting-edge quantum computing,with a particular focus on quadratic unconstrained binary optimization(QUBO)and quantum machine learning(QML).We introduce the loop framework for QUBO-empowered materials design,including constructing high-quality datasets that capture critical material properties,employing tailored computational methods for precise material modeling,developing advanced figures of merit to evaluate performance metrics,and utilizing quantum optimization algorithms to discover optimal materials.In addition,we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations.The review also highlights advanced active learning strategies that integrate quantum artificial intelligence,offering a more efficient pathway to explore the vast,complex material design space.Finally,we discuss the key challenges and future opportunities for QML in material design,emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.展开更多
文摘As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.
基金supported by the Collaboration for Unprecedented Success and Excellence(CUSE)Grant at Syracuse University under Ⅱ-3267-2022by the U.S.National Science Foundation under Award No.1949372.
文摘Buildings use a large amount of energy in the United States.It is important to optimally manage and coordinate the resources across building and power distribution networks to improve overall efficiency.Optimizing the power grid with discrete variables was very challenging for traditional computers and algorithms,as it is an NP-hard problem.In this study,we developed a new optimization solution based on quantum computing for BTG integration.We first used MPC for building loads connected with a commercial distribution grid for cost reduction.Then we used discretization and Benders Decomposition methods to reformulate the problem and decompose the continuous and discrete variables,respectively.We used D-Wave quantum computer to solve dual problems and used conventional algorithm for primal problems.We applied the proposed method to an IEEE 9-bus network with 3 commercial buildings and over 300 residential buildings to evaluate the feasibility and effectiveness.Compared with traditional optimization methods,we obtained similar solutions with some fluctuations and improved computational speed from hours to seconds.The time of quantum computing was greatly reduced to less than 1% of traditional optimization algorithm and software such as MATLAB.Quantum computing has proved the potential to solve large-scale discrete optimization problems for urban energy systems.
基金supported by the Special Zone Project of National Defense Innovation and the Science and Technology Program of Education Department of Jiangxi Province(No.GJJ171503).
文摘In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.
基金supported by the Shanghai Key Fundamental Research Grant(No.21JC1403300).
文摘The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing,fabrication,and characterization.This review introduces a comprehensive methodology for materials design using cutting-edge quantum computing,with a particular focus on quadratic unconstrained binary optimization(QUBO)and quantum machine learning(QML).We introduce the loop framework for QUBO-empowered materials design,including constructing high-quality datasets that capture critical material properties,employing tailored computational methods for precise material modeling,developing advanced figures of merit to evaluate performance metrics,and utilizing quantum optimization algorithms to discover optimal materials.In addition,we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations.The review also highlights advanced active learning strategies that integrate quantum artificial intelligence,offering a more efficient pathway to explore the vast,complex material design space.Finally,we discuss the key challenges and future opportunities for QML in material design,emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.
