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.
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.
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.
The core of this article is to construct an effective QUBO model to solve the problem of cloud edge end computing resource allocation, including two key scenarios: optimization of server maximum computing demand coverage and optimization of cloud edge server computing network layout. Each scenario provides a detailed description of the model's process from analysis, establishment to solution, overcoming several major difficulties in establishing a QUBO model, including using binary variables to express the objective function, transforming the objective function into QUBO expressions, and transforming inequality constraints. A scheme for the number of constraint relaxation factors is introduced, which effectively reduces the number of quantum bits and computational complexity. Finally, statistical analysis of the solution results, non parametric testing based on data characteristics, and detailed analysis are conducted, demonstrating the potential application of the QUBO model and quantum computing in dealing with complex combinatorial optimization problems. During the solving process, China's first optical quantum computing power cloud platform and corresponding quantum computing library Kaiwu SDK were utilized for research. Multiple solvers were used and their performance was compared to select the best performing taboo search solver, and then detailed results were calculated. The final results were fully visualized to make them more intuitive. The results indicate that quantum computing can provide an efficient, accurate, and stable solution for cloud edge end computing resource allocation. Through these studies, this article not only confirms the efficiency of quantum computing in solving specific optimization problems, but also provides some experience and reference for exploring the application of quantum computing in a wider field.
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.
Quantum annealing (QA) offers a physics-inspired approach for such subproblems by mapping them onto an Ising Hamiltonian, which is algebraically equivalent to a Quadratic Unconstrained Binary Optimization (QUBO) model executable on quantum annealers or hybrid solvers. However, their practical employment in wireless systems critically depends on the specific problem formulation, on the particular variable encoding, constraint handling, solver selection, hardware embedding, and fair benchmarking. This paper develops wireless-oriented QUBO modeling guidelines for QA-assisted resource allocation. We first present a beginner-friendly toy example in which a terrestrial-aerial resource-allocation problem with a fractional signal-to-interference-plus-noise ratio (SINR) maximization objective is reformulated into a two-bit QUBO via Boolean interpolation, converted into an Ising representation, and decoded back into a wireless scheduling decision. The same scheduling is applicable to UAV, aircraft, satellite, and terrestrial links in SAGIN systems. We then review the QA foundations and solver ecosystems, including binary quadratic, discrete quadratic, constrained quadratic, and hybrid models from the perspective of NG wireless optimization. Furthermore, we discuss both binary and discrete encoding, penalty-based constraint conversion, embedding overhead, solution decoding, and feasibility verification. A QUBO Compatibility Index is introduced as a lightweight diagnostic metric for assessing the associated QUBO formulation readiness, rather than a predictor of runtime or solution quality. Finally, a case study on max-min uplink power control in cell-free massive MIMO illustrates how a fairness-oriented wireless problem can be discretized, constrained, converted into the concept of a QUBO, and evaluated against classical baselines. A host of key challenges and future directions are also discussed.
To run an algorithm on a quantum computer, one must choose an assignment from logical qubits in a circuit to physical qubits on quantum hardware. This task of initial qubit placement, or qubit allocation, is especially important on present-day quantum computers which have a limited number of qubits, connectivity constraints, and varying gate fidelities. In this work we formulate and implement the qubit placement problem as a quadratic, unconstrained binary optimization (QUBO) problem and solve it using simulated annealing to obtain a spectrum of initial placements. Compared to contemporary allocation methods available in t|ket$\rangle $ and Qiskit, the QUBO method yields allocations with improved circuit depth for $>$50% of a large set of benchmark circuits, with many also requiring fewer CX gates.
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.