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 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 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.
The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.
As systems become more complex and large-scale, there is a need for more effective scheduling algorithms to maximize resource usage and minimize job completion times. The older methods of scheduling like First-Come-First-Serve (FCFS) and Round Robin (RR), although easier to compute, tend to perform poorly in dynamic scenarios because they cannot manage intricate constraints and multi-objective optimization efficiently. As traditional methods approach their limit of performance, quantum computing presents a potentially fruitful avenue for conquering combinatorial challenges based on its ability to examine many states at once. This work proposes a quantum-aided scheduling framework that employs Quadratic Unconstrained Binary Optimization (QUBO) formulations and quantum solvers for the purpose of improving job scheduling in distributed environments. The original contribution is the marrying of quantum optimization (with IBM Qiskit's QAOA and D-Wave's hybrid solvers) with traditional distributed computing frameworks such as Apache Spark. Google Cluster Workload Traces are employed to mimic real-world situations, and jobs are described by CPU, memory requirements, and arrival times. Once the scheduling problem has been formulated into a QUBO model, quantum algorithms produce the optimized job-to-machine assignments. The outcomes are compared against baseline classical schedulers on performance metrics including make span, throughput, and resource utilization. The experiment demonstrates that quantum-assisted scheduling significantly improves makespan and enhances resource utilization, particularly under high workloads. It also provides evidence of the feasibility of integrating quantum solvers with existing big data infrastructures.
Energy communities are collectives that manage their own production, distribution, and consumption of energy, usually from renewable sources. To increase efficiency and sustainability, the scheduling of the loads on the basis of the user needs, the electricity prices, the availability of local renewable energy and the battery storage capacity must be optimized. Quantum computing, leveraging principles like superposition and entanglement, offers new computational power for complex optimization tasks. This paper lays the basis for exploring the potential of quantum computing in optimizing the operation of energy communities by modeling the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
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.