Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Journal of Ocean Engineering and Marine Energy

Possible application of quantum computing in the field of ocean engineering: optimization of an offshore wind farm layout with the Ising model

Hiroshi Kagemoto

Published Aug 31, 2024• 3 citations
Publisher

Abstract

Abstract As a possible application of quantum computing in the field of ocean engineering, an attempt is made to identify the optimum layout of an offshore wind farm with the quasi-quantum annealing method while assuming a wake model that conforms to the standard Jensen–Katic wake model but can be implemented in the Ising model. The optimum layouts obtained in the present study are compared with those identified with other conventional methods such as the genetic algorithm. It is confirmed that the quasi-quantum annealing method works as well as other methods in identifying the optimum layouts, while the optimum layout of an offshore wind farm composed of even more than 100 wind turbines can be identified with reasonable CPU time.

DOI
10.1007/s40722-024-00345-x

Related papers

Leveraging Quantum Annealing for Layout Optimization

Luca Nigro, Simone Sala, Alfonso Amendola +1 more

Abstract Layout optimization problems involve finding the optimal arrangement of elements in order to maximize efficiency. For instance, the wind farm layout optimization (WFLO) problem consists of the best turbine placement to maximize energy production while minimizing wake losses. As its nonlinear and combinatorial nature makes it challenging for traditional optimization methods, alternative approaches such as quantum annealing and quantum‐classical hybrid methods offer a promising alternative for tackling such complex problems. Here, WFLO is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem using the Jensen wake model. A quantum annealer is compared, the Gurobi solver, and the Quantum Approximate Optimization Algorithm (QAOA). The quantum annealer provides solutions one order of magnitude faster than Gurobi with at most 3% lower power output, making it suitable for rapid suboptimal approximations. These findings highlight the trade‐off between the quality of the solution and the computational time and demonstrate how quantum methods, especially when combined with classical solvers, can contribute to efficient renewable energy optimization.

Advanced Quantum Technologies•Sep 29, 2025•1 citation

A Novel Quantum Algorithm for Solving Optimization Problems in Electrical Engineering

Ajay Sharma, Kuldeep Sharma, Kanchan Yadav +3 more

The ability of theoretical and monadic quantum models against number comparison to conventional Quantum Genetic Algorithm (QGA), quantitative particle swarm optimization, ant colony groups with simulated annealing types is analyzed in the context of electrical engineering. Several experiments were done, involving a collection of multiple data sets standard for the proposed circuit layout within power distribution and signal processing applications. In regard to comparative analysis, each algorithm presents its particular strengths for QGA-competitive convergence speed; ͟QPSO – quick conversion of individuals into a global optimum during evolution’s course and robust solutions quality in an unstable environment without tuning. The related works of the proposed algorithms include evaluating it against metaheuristics for power systems, nature-inspired hybrid heuristic approaches as well as physics motivated optimization schemes. The paper emphasizes the superiority of quantum algorithms over their classical counterparts, which is a major innovation in optimization space. This detailed analysis contributes to further comprehending the potentials of quantum computing in overcoming tough optimization challenges faced by electrical engineers.

International Conference on Innovative Practices in Technology and Management (ICIPTM)•Feb 21, 2024•0 citations

Investigating Techniques to Optimise the Layout of Turbines in a Windfarm Using a Quantum Computer

James Hancock, Matthew J. Craven, Craig Mcneile A +1 more

This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in ... | Find, read and cite all the research you need on Tech Science Press

arXiv:2312.13123•Journal of quantum computing•Jan 1, 2025•1 citation

White Paper: Quantum-AI Hybrid Approach for Wind Farm Layout Optimization

Vardaan Sahgal, Jitesh Lalwani, Babita Jajodia

The purpose of this white paper is to discuss how Quantum and AI hybrid approaches can be used to optimize wind farm layouts such that energy generation is maximized.

•Oct 6, 2022•0 citations

Optimization of quantum computing models inspired by D-wave quantum annealing

Baonan Wang, Feng Hu, Chao Wang

With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important. The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations. This study constructs a new Quantum-Inspired Annealing (QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one. Through various configurations of the 1D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart (22% in case). This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.

Tsinghua Science & Technology•Jun 12, 2020•13 citations

Application Overview of Quantum Computing for Gas Turbine Design and Optimization

Aurthur Vimalachandran Thomas Jayachandran

Conceptual designs require optimization methods to identify the best fit in the system. The article investigates the application of quantum computation in gas turbine design and simulation problems with current technologies, approaches and potential capabilities. Quantum optimization algorithms and quantum annealers help in predicting overall efficiency and optimizing various operating parameters of the gas turbine. A comparison of both classical and quantum computers has been discussed briefly. The classical model challenges are mitigated with the use of quantum computation. A novel hybrid model for simulating gas turbines has been proposed, which consists of a combination of both physics and machine learning to eliminate few of the critical problems faced. This review elaborates application of quantum computing based machine learning for design and optimization of a gas turbine. The overall states of the gas paths of gas turbines could be analyzed using the quantum computing model in the future.

AI Computer Science and Robotics Technology•Aug 1, 2022•0 citations
Abstract As a possible application of quantum computing in the field of ocean engineering, an attempt is made to identify the optimum layout of an offshore wind farm with the quasi-quantum annealing method while assuming a wake model that conforms to the standard Jensen–Katic wake model but can be implemented in the Ising model. The optimum layouts obtained in the present study are compared with those identified with other conventional methods such as the genetic algorithm. It is confirmed that the quasi-quantum annealing method works as well as other methods in identifying the optimum layouts, while the optimum layout of an offshore wind farm composed of even more than 100 wind turbines can be identified with reasonable CPU time.
Abstract Layout optimization problems involve finding the optimal arrangement of elements in order to maximize efficiency. For instance, the wind farm layout optimization (WFLO) problem consists of the best turbine placement to maximize energy production while minimizing wake losses. As its nonlinear and combinatorial nature makes it challenging for traditional optimization methods, alternative approaches such as quantum annealing and quantum‐classical hybrid methods offer a promising alternative for tackling such complex problems. Here, WFLO is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem using the Jensen wake model. A quantum annealer is compared, the Gurobi solver, and the Quantum Approximate Optimization Algorithm (QAOA). The quantum annealer provides solutions one order of magnitude faster than Gurobi with at most 3% lower power output, making it suitable for rapid suboptimal approximations. These findings highlight the trade‐off between the quality of the solution and the computational time and demonstrate how quantum methods, especially when combined with classical solvers, can contribute to efficient renewable energy optimization.
The ability of theoretical and monadic quantum models against number comparison to conventional Quantum Genetic Algorithm (QGA), quantitative particle swarm optimization, ant colony groups with simulated annealing types is analyzed in the context of electrical engineering. Several experiments were done, involving a collection of multiple data sets standard for the proposed circuit layout within power distribution and signal processing applications. In regard to comparative analysis, each algorithm presents its particular strengths for QGA-competitive convergence speed; ͟QPSO – quick conversion of individuals into a global optimum during evolution’s course and robust solutions quality in an unstable environment without tuning. The related works of the proposed algorithms include evaluating it against metaheuristics for power systems, nature-inspired hybrid heuristic approaches as well as physics motivated optimization schemes. The paper emphasizes the superiority of quantum algorithms over their classical counterparts, which is a major innovation in optimization space. This detailed analysis contributes to further comprehending the potentials of quantum computing in overcoming tough optimization challenges faced by electrical engineers.
With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important. The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations. This study constructs a new Quantum-Inspired Annealing (QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one. Through various configurations of the 1D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart (22% in case). This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
Conceptual designs require optimization methods to identify the best fit in the system. The article investigates the application of quantum computation in gas turbine design and simulation problems with current technologies, approaches and potential capabilities. Quantum optimization algorithms and quantum annealers help in predicting overall efficiency and optimizing various operating parameters of the gas turbine. A comparison of both classical and quantum computers has been discussed briefly. The classical model challenges are mitigated with the use of quantum computation. A novel hybrid model for simulating gas turbines has been proposed, which consists of a combination of both physics and machine learning to eliminate few of the critical problems faced. This review elaborates application of quantum computing based machine learning for design and optimization of a gas turbine. The overall states of the gas paths of gas turbines could be analyzed using the quantum computing model in the future.