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

Academic Journal of Computing & Information Science

Optimization Research of Smart Mine Operation Scheme Based on QUBO Model and Quantum Annealing Algorithm

Shiqi Zhou

Published Jan 1, 2024• 1 citation
Publisher

Abstract

This paper analyzes the equipment configuration of the smart mine, builds the corresponding Quadratic Unconstrained Binary Optimization (QUBO) model according to different application scenarios, and analyzes and solves the QUBO model by using the simulated annealing solver and the CIM simulator, to explore the optimal operation scheme of the smart mine under different scenarios. Firstly, under the budget constraint, the objective function is to maximize the sum of the discounted long-term profits of various excavators, and at the same time to meet the constraints such as budget limitation and type limitation, the mathematical model is established and transformed into the QUBO model, and then analytically solved to derive the optimal operation plan of the smart mine under this scenario. Subsequently, the mathematical model is reconstructed to derive the optimal operation scenario and expected profit, taking into account the factor of years of life. In terms of the solution method, kaiwu sdk shows good performance for small-scale problems, but it is limited by memory and less effective when facing large-scale problems. Therefore, this paper proposes the subQUBO method, which combines quantum and classical computing, as a potential improvement path. This study not only provides an optimization solution for the operation of smart mines, but also provides new ideas for solving similar large-scale optimization problems.

DOI
10.25236/ajcis.2024.070522

Related papers

Research on Smart Mine Equipment Procurement Optimization Based on Simulated Annealing and Quantum Computing

Shanshan Yu, Yuanfeng Deng, Chuyang Kang +1 more

With the development of quantum computing, traditional combinatorial optimization problems have found new solving methods. This paper studies the excavator and mining truck matching optimization problem based on the QUBO model, aiming to optimize equipment configuration and operational plans by maximizing long-term profit. An integer optimization model is first established and converted into a QUBO model, which is solved using the simulated annealing algorithm. Furthermore, quantum computing is explored through the Coherent Ising Machine (CIM) simulator to examine its application in large-scale combinatorial optimization. The results show that the CIM simulator outperforms traditional algorithms in both accuracy and speed, significantly enhancing computational efficiency. Based on simulated annealing and quantum computing, an optimized procurement plan is provided, yielding a maximum total profit of 500 million yuan. The experiments also demonstrate the potential of quantum computing in optimizing equipment configuration in smart mining, driving the intelligent evolution of the field.

Highlights in Science Engineering and Technology•Mar 25, 2025•0 citations

Application study of simulated annealing solver and CIM simulator based on QUBO model using kaiwu SDK

Ming Chi

In this paper, a methodology for finding the optimal solution by building a QUBO (Quadratic Unconstrained Binary Optimisation) model and using a combination of the Simulated Annealing algorithm and the CIM (Coherent Equivalent Machine) simulator in the Open Matter SDK will be presented. Firstly, the QUBO model was chosen because of its significant advantages in dealing with logical inference and combinatorial optimisation problems. The QUBO model provides an efficient solution method by transforming the problem into a quadratic unconstrained binary optimisation problem. In solving this problem, the first step is to define the objective and constraints of the problem, where the objective is to satisfy the relevant requirements and make the best of them, and the constraints are the relevant parameters provided by the data, and to build a mathematical model that matches the QUBO model. Next, a simulated annealing algorithm and a CIM simulator were used to solve this QUBO problem. By combining the QUBO model with these two algorithms, the optimal solution can be searched efficiently. Finally, the optimal solution is derived from the solution results. These scenarios not only satisfy the constraints but also achieve the given objective by optimising the combination of decision variables.

International Conference on Electronics and Devices, Computational Science (ICEDCS)•Sep 23, 2024•1 citation

Research on Modeling Quantum Computing Applications in Mine Equipment Configuration and Operations

Tao Jiang, Ni Fang, Mengyao Deng

This paper explores the optimization of mine equipment configuration and operations using quantum computing and the Quadratic Unconstrained Binary Optimization (QUBO) model. It highlights quantum computing's ability to enhance problem-solving efficiency and solution quality, especially for complex and multivariable optimization problems, compared to traditional methods.The research focuses on two main areas: optimizing equipment configuration and scheduling operations. For equipment configuration, potential schemes are analyzed for complexity and cost-effectiveness, with validation on quantum hardware. For operation scheduling, equipment data is analyzed to create an optimization model, demonstrating improved efficiency through simulations.The study concludes that quantum computing significantly enhances efficiency and solution quality for mine equipment management, proving its superiority over traditional methods.

Transactions on Computer Science and Intelligent Systems Research•Aug 12, 2024•0 citations

Quantum Optimization Using QAOA for Constrained Binary Problems: A Mine Planning Case Study

Bairon Rojas-Castro, Aldo Quelopana, Pablo A. Miranda-Gonzalez

This paper presents the application of a quantum optimization technique to a mining problem. A mine planning problem in an open-pit mine is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved using the Quantum Approximate Optimization Algorithm (QAOA). The problem seeks to maximize the monetary benefit while respecting precedence constraints between blocks and extraction capacity limitations. The methodology is validated through a fictitious copper deposit case study, where the quantum algorithm identifies the optimal extraction, a result validated with Gurobi solver. Although the approach demonstrates technical feasibility, the scalability limitations of current quantum simulators restrict applications to large-scale problems. This work establishes fundamental foundations for applying quantum computing to mine planning challenges and represents a step toward leveraging quantum computational advantages for NP-hard problems in the mining industry.

International Conference of the Chilean Computer Science Society (SCCC)•Oct 1, 2025•0 citations

A Quantum Annealing Algorithm for the Resource-Constrained Excavator Procurement Problem

Linhao Chang, Meng Xu, Lifei Guo +4 more

This paper investigates a constrained excavator procurement problem (RCEPP), in which the budget is restricted and the performance of the excavators in collaboration with the pre-purchased mining trucks is considered. The RCEPP consists of deciding the purchase quantity of excavators to maximize the profit from the mining system composed of the imported excavators and the existing mining trucks. A mixed integer programming model (MIP) is established to address the RCEPP. In line with the specifications of quantum computers, a quadratic unconstrained binary optimization model is developed via reformulating the MIP. The scheduling scheme is coded into the ground state of the Hamiltonian operator, and the problem is solved using an optical coherent Ising machine (CIM). The experimental results indicate that, compared to traditional computation methods, quantum computers are capable of achieving better global optimal solutions. It has great application potential.

IEEE International Conference on Networking, Sensing and Control (ICNSC)•Oct 18, 2024•1 citation

A QUBO model-based approach for solving the computational resource allocation problem and its application analysis

Ming Chi

This paper discusses the solution method of the computational resource allocation problem based on the QUBO model, and analyzes the application in combination with the simulated annealing algorithm and the CIM simulator. The reasons for choosing the QUBO model and its advantages in logical reasoning are first introduced, and then the core principles of the QUBO model are analyzed, including its relationship with quadratic unconstrained optimization problems. Then the principles of simulated annealing algorithm and CIM simulator and their applications in problem solving are discussed in detail. In terms of specific problem solving, taking the optimal position of base station setting and the optimal path of overhauling as examples, the problem definition, decision variables, the establishment of QUBO model, and the solving process are described respectively, and the solving process is demonstrated through flowcharts. Finally, the solution results of the base station setup and maintenance path problems are given and the model is evaluated.

Highlights in Science Engineering and Technology•May 20, 2024•0 citations
This paper analyzes the equipment configuration of the smart mine, builds the corresponding Quadratic Unconstrained Binary Optimization (QUBO) model according to different application scenarios, and analyzes and solves the QUBO model by using the simulated annealing solver and the CIM simulator, to explore the optimal operation scheme of the smart mine under different scenarios. Firstly, under the budget constraint, the objective function is to maximize the sum of the discounted long-term profits of various excavators, and at the same time to meet the constraints such as budget limitation and type limitation, the mathematical model is established and transformed into the QUBO model, and then analytically solved to derive the optimal operation plan of the smart mine under this scenario. Subsequently, the mathematical model is reconstructed to derive the optimal operation scenario and expected profit, taking into account the factor of years of life. In terms of the solution method, kaiwu sdk shows good performance for small-scale problems, but it is limited by memory and less effective when facing large-scale problems. Therefore, this paper proposes the subQUBO method, which combines quantum and classical computing, as a potential improvement path. This study not only provides an optimization solution for the operation of smart mines, but also provides new ideas for solving similar large-scale optimization problems.
With the development of quantum computing, traditional combinatorial optimization problems have found new solving methods. This paper studies the excavator and mining truck matching optimization problem based on the QUBO model, aiming to optimize equipment configuration and operational plans by maximizing long-term profit. An integer optimization model is first established and converted into a QUBO model, which is solved using the simulated annealing algorithm. Furthermore, quantum computing is explored through the Coherent Ising Machine (CIM) simulator to examine its application in large-scale combinatorial optimization. The results show that the CIM simulator outperforms traditional algorithms in both accuracy and speed, significantly enhancing computational efficiency. Based on simulated annealing and quantum computing, an optimized procurement plan is provided, yielding a maximum total profit of 500 million yuan. The experiments also demonstrate the potential of quantum computing in optimizing equipment configuration in smart mining, driving the intelligent evolution of the field.
In this paper, a methodology for finding the optimal solution by building a QUBO (Quadratic Unconstrained Binary Optimisation) model and using a combination of the Simulated Annealing algorithm and the CIM (Coherent Equivalent Machine) simulator in the Open Matter SDK will be presented. Firstly, the QUBO model was chosen because of its significant advantages in dealing with logical inference and combinatorial optimisation problems. The QUBO model provides an efficient solution method by transforming the problem into a quadratic unconstrained binary optimisation problem. In solving this problem, the first step is to define the objective and constraints of the problem, where the objective is to satisfy the relevant requirements and make the best of them, and the constraints are the relevant parameters provided by the data, and to build a mathematical model that matches the QUBO model. Next, a simulated annealing algorithm and a CIM simulator were used to solve this QUBO problem. By combining the QUBO model with these two algorithms, the optimal solution can be searched efficiently. Finally, the optimal solution is derived from the solution results. These scenarios not only satisfy the constraints but also achieve the given objective by optimising the combination of decision variables.
This paper explores the optimization of mine equipment configuration and operations using quantum computing and the Quadratic Unconstrained Binary Optimization (QUBO) model. It highlights quantum computing's ability to enhance problem-solving efficiency and solution quality, especially for complex and multivariable optimization problems, compared to traditional methods.The research focuses on two main areas: optimizing equipment configuration and scheduling operations. For equipment configuration, potential schemes are analyzed for complexity and cost-effectiveness, with validation on quantum hardware. For operation scheduling, equipment data is analyzed to create an optimization model, demonstrating improved efficiency through simulations.The study concludes that quantum computing significantly enhances efficiency and solution quality for mine equipment management, proving its superiority over traditional methods.
This paper presents the application of a quantum optimization technique to a mining problem. A mine planning problem in an open-pit mine is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved using the Quantum Approximate Optimization Algorithm (QAOA). The problem seeks to maximize the monetary benefit while respecting precedence constraints between blocks and extraction capacity limitations. The methodology is validated through a fictitious copper deposit case study, where the quantum algorithm identifies the optimal extraction, a result validated with Gurobi solver. Although the approach demonstrates technical feasibility, the scalability limitations of current quantum simulators restrict applications to large-scale problems. This work establishes fundamental foundations for applying quantum computing to mine planning challenges and represents a step toward leveraging quantum computational advantages for NP-hard problems in the mining industry.
This paper investigates a constrained excavator procurement problem (RCEPP), in which the budget is restricted and the performance of the excavators in collaboration with the pre-purchased mining trucks is considered. The RCEPP consists of deciding the purchase quantity of excavators to maximize the profit from the mining system composed of the imported excavators and the existing mining trucks. A mixed integer programming model (MIP) is established to address the RCEPP. In line with the specifications of quantum computers, a quadratic unconstrained binary optimization model is developed via reformulating the MIP. The scheduling scheme is coded into the ground state of the Hamiltonian operator, and the problem is solved using an optical coherent Ising machine (CIM). The experimental results indicate that, compared to traditional computation methods, quantum computers are capable of achieving better global optimal solutions. It has great application potential.
This paper discusses the solution method of the computational resource allocation problem based on the QUBO model, and analyzes the application in combination with the simulated annealing algorithm and the CIM simulator. The reasons for choosing the QUBO model and its advantages in logical reasoning are first introduced, and then the core principles of the QUBO model are analyzed, including its relationship with quadratic unconstrained optimization problems. Then the principles of simulated annealing algorithm and CIM simulator and their applications in problem solving are discussed in detail. In terms of specific problem solving, taking the optimal position of base station setting and the optimal path of overhauling as examples, the problem definition, decision variables, the establishment of QUBO model, and the solving process are described respectively, and the solving process is demonstrated through flowcharts. Finally, the solution results of the base station setup and maintenance path problems are given and the model is evaluated.