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 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.
Abstract The selection of crude oil feedstock is a critical, high-impact optimization problem in the petrochemical industry, directly influencing refinery profitability and environmental footprint. Traditional optimization methods face scalability challenges as the complexity and number of constraints increase. This paper presents an initial study on the application of quantum computing to solve a binary feedstock optimization model. We formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model and employ the Quantum Approximate Optimization Algorithm (QAOA) to find low-cost solutions. To manage the probabilistic nature of quantum results, we introduce Conditional Value at Risk (CVaR) as a post-processing technique to select robust, high-quality solutions. Our results on a representative small-scale problem demonstrate the viability of this hybrid quantum-classical approach, showing its potential to identify near-optimal solutions. We conclude with a discussion of current limitations and an open call for collaboration to further explore quantum computing applications in the energy sector.
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.
While the ultimate goal of solving computationally intractable problems is to find a provably optimal solutions, practical constraints of real-world scenarios often necessitate focusing on efficiently obtaining high-quality, near-optimal solutions. The Quantum Approximate Optimization Algorithm (QAOA) is a state-of-the-art hybrid quantum-classical approach for tackling these challenging problems that are encoded using quadratic and higher-order unconstrained binary optimization problems (QUBO and HUBO). We present SCOOP, a novel QAOAbased framework for solving constrained optimization problems. SCOOP transforms a constrained problem into an unconstrained counterpart, forming SCOOP problem twins. The QAOA quantum algorithm operates on the unconstrained twin to identify potential optimal and near-optimal solutions. Effective classical post-processing reduces the solution set to the constrained problem space. Our SCOOP approach is solution-enhanced, objective-function-compatible, and scalable. We demonstrate the framework on three NP-hard problems, Minimum Dominating Set, Minimum Maximal Matching, and Minimum Set Cover appearing in practical application domains such as resource allocation, communication networks, and machine learning. We validate SCOOP's feasibility and effectiveness on Xanadu PennyLane simulators.
The Quantum Approximate Optimization Algorithm (QAOA) has shown promise in solving combinatorial optimization problems by leveraging quantum computational power. We propose a simple approach, the Two-Step QAOA, which aims to improve the effectiveness of QAOA by decomposing problems with k-hot encoding QUBO (Quadratic Unconstrained Binary Optimization) formulations. By identifying and separating the problem into two stages, we transform soft constraints into hard constraints, simplifying the generation of initial conditions and enabling more efficient optimization. The method is particularly beneficial for tackling complex societal problems that often involve intricate constraint structures.
Quantum computers are expected to offer advantages in solving optimization problems challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO problems on near-term quantum devices. In this paper, we evaluate the performance of both the standard QAOA and the adaptive derivative assembled problem tailored QAOA (ADAPT-QAOA) to solve QUBO problems of varying sizes and hardnesses for financial feature selection problems. Our main observation is that ADAPT-QAOA achieves substantially higher approximation ratios than standard QAOA for harder feature-selection problems (α = 0.6) with statistically significant improvements observed for problem sizes n = 6 and 14. However, the standard QAOA remains competitive for simpler problems. Additionally, we evaluate the practical feasibility and limitations of QAOA through a hardware-aware scaling analysis based on the real-device calibration data for various hardware platforms. We estimate that standard QAOA implementation on superconducting quantum computers provides a shorter time-to-solution compared to trapped-ion devices, while trapped-ion devices yield more favorable error rates. Our findings provide a comprehensive overview of the challenges, trade-offs, and strategies for deploying QAOA-based methods on near-term quantum hardware.
Portfolio optimization, a core combinatorial optimization problem in quantitative finance, requires balancing return maximization and risk minimization within discrete asset sets. Classical algorithms often struggle with scalability and local optima traps when handling large-scale asset pools, limiting their practicality in dynamic markets. This study proposes a complete Quantum Approximate Optimization Algorithm (QAOA) workflow for portfolio optimization, from problem formalization to experimental validation. We first transform the portfolio optimization problem into a Quadratic Unconstrained Binary Optimization (QUBO) framework, then design a parameterized QAOA circuit with adjustable layers to balance expressiveness and noise resilience. We propose a methodology using Qiskit for quantum simulation and classical optimizers for parameter tuning, we propose implementing the algorithm on real-world S&P 500 stock data. We could also analyze the impact of quantum hardware noise and propose mitigation strategies, providing actionable insights for Noisy intermediate-scale quantum computing (NISQ)-era implementations. This work bridges quantum algorithm design with real-world financial applications, offering a theoretical framework for quantum-driven portfolio optimization.