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Academic Journal of Computing & Information Science, 2024, 7(5); doi: 10.25236/AJCIS.2024.070522.

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


Shiqi Zhou, Yita Han, Xvdong Wang

Corresponding Author:
Shiqi Zhou

Henan Normal University of Science and Technology, Qinhuangdao, China


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.


QUBO Model, Simulated annealing solver, CIM Simulator, quantum annealing algorithm

Cite This Paper

Shiqi Zhou, Yita Han, Xvdong Wang. Optimization Research of Smart Mine Operation Scheme Based on QUBO Model and Quantum Annealing Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 168-173. https://doi.org/10.25236/AJCIS.2024.070522.


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