Efficient management of water resource systems requires solving complex optimization problems involving nonlinear dynamics, large datasets, and multiple operational constraints. Traditional optimization techniques such as linear programming, dynamic programming, and evolutionary algorithms have been widely applied in water resource management; however, these methods often face computational challenges when applied to large-scale hydrological systems. This paper proposes a hybrid quantum-classical optimization framework for water resource management using topological qubit models. The water allocation problem is formulated as a network-based optimization model and transformed into a quadratic unconstrained binary optimization (QUBO) problem suitable for quantum optimization algorithms. A hybrid computational approach combining classical hydrological simulation with quantum optimization is developed to improve computational efficiency. Simulation results demonstrate that the proposed model achieves faster convergence and lower operational cost compared with classical optimization methods. The study shows that quantum computing provides a promising computational approach for solving complex water resource optimization problems. Future work will focus on implementing the proposed model on real quantum hardware and integrating real-world hydrological datasets.
Efficient management of water resource systems requires solving complex optimization problems involving nonlinear dynamics, large datasets, and multiple operational constraints. Traditional optimization techniques such as linear programming, dynamic programming, and evolutionary algorithms have been widely applied in water resource management; however, these methods often face computational challenges when applied to large-scale hydrological systems. This paper proposes a hybrid quantum-classical optimization framework for water resource management using topological qubit models. The water allocation problem is formulated as a network-based optimization model and transformed into a quadratic unconstrained binary optimization (QUBO) problem suitable for quantum optimization algorithms. A hybrid computational approach combining classical hydrological simulation with quantum optimization is developed to improve computational efficiency. Simulation results demonstrate that the proposed model achieves faster convergence and lower operational cost compared with classical optimization methods. The study shows that quantum computing provides a promising computational approach for solving complex water resource optimization problems. Future work will focus on implementing the proposed model on real quantum hardware and integrating real-world hydrological datasets.
Efficient irrigation scheduling is a major challenge in precision agriculture due to increasing water scarcity, climate variability, and growing agricultural demand. Conventional irrigation strategies based on fixed thresholds, heuristic rules, or classical optimization techniques often suffer from scalability limitations, inefficient resource allocation, and poor adaptability under dynamic environmental conditions. This paper presents a hybrid quantum-classical optimization framework for intelligent irrigation scheduling using the Quantum Approximate Optimization Algorithm (QAOA). The irrigation decision problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model integrating soil moisture conditions, irrigation constraints, and water allocation objectives. The proposed framework employs variational quantum optimization to determine optimal irrigation decisions while minimizing unnecessary water consumption. Experimental evaluations conducted using Qiskit-based simulations demonstrate that the proposed QAOA framework successfully generates irrigation decisions fully consistent with classical threshold-based optimization for the evaluated dataset. Comprehensive visualization analyses including soil moisture distribution, irrigation decision mapping, threshold boundary analysis, and water utilization comparison validate the correctness and interpretability of the proposed optimization mechanism. Although the current experimental setup primarily serves as a proof-of-concept validation, the results indicate strong scalability potential for future large-scale agricultural systems involving complex multi-objective optimization constraints. The proposed framework establishes a foundational architecture for future quantum-enabled precision agriculture systems integrating IoT sensing, real-time environmental monitoring, and intelligent resource optimization for sustainable smart farming applications.
Optimization problems are widely encountered in real-world domains such as energy systems, financial markets, transportation networks, supply chain logistics, manufacturing processes, and e-commerce platforms. Traditional classical computing methods often struggle to solve large combinatorial optimization problems due to the exponential growth of the solution space. Quantum computing has emerged as a promising approach to address these challenges by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum hardware is limited by noise, restricted qubit availability, and scalability issues, making fully quantum solutions impractical for large-scale applications. To overcome these limitations, this research proposes a hybrid quantum–classical supercomputing framework for solving complex optimization problems across multiple domains. The framework integrates classical data processing and optimization techniques with quantum-inspired algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), to efficiently explore large solution spaces. Real-world datasets from energy systems, supply chain logistics, financial markets, transportation networks, manufacturing processes, and e-commerce platforms are used for experimental evaluation. The results demonstrate that the hybrid optimization approach can generate higher-quality solutions and improved optimization accuracy compared to traditional classical methods. These findings highlight the potential of hybrid quantum–classical systems as an effective and scalable solution for solving complex optimization problems in modern computational applications.
• We propose a new hybrid quantum-classical algorithm for solving the UC problem. • The hybrid model successfully optimizes 24-hour UC systems from 10 to 1000 units. • The model outperforms classical metaheuristics (GA and SA) and classical MINLP. • This represents an improvement over previous QCUC methods, a step to real-world use. Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch‑and‑Bound, Branch‑and‑Cut, dynamic programming and mixed‑integer linear programming (MILP), remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to initial conditions, and can become prohibitively time‑consuming for large‑scale systems. In this paper, we introduce a quantum‑classical hybrid algorithm for UC, and other resource scheduling problems by extension, that leverages Benders decomposition to decouple binary commitment decisions from continuous economic dispatch. The binary “master problem” is formulated as a quadratic unconstrained binary optimization (QUBO) model and solved on a quantum annealer. The continuous “subproblem,” which minimizes generation costs, with Lagrangian cuts feeding back to the master until convergence. We evaluate our hybrid framework on systems scaled from 10 to 1,000 generation units. Compared against a classical mixed‑integer nonlinear programming (MINLP) baseline, the hybrid algorithm achieves a consistently lower computation‑time growth rate and maintains an absolute optimality gap below 1.63%. These results demonstrate that integrating quantum annealing within a hybrid quantum-classical Benders decomposition loop can significantly accelerate large‑scale resource scheduling without sacrificing solution quality, pointing toward a viable path for addressing the escalating complexity of modern power grids.
The assignment of resources dynamically is a decisive issue in the new paradigms of computations including cloud, edge, fog computing, and the Internet of Things (IoT). These are heterogeneous resource environments, decentralized control environments, and overload environments that vary at a rapid pace and require adaptive and intelligent allocation schemes. The paper suggests a hybrid quantum-game-theoretic model, which combines quantum optimization approaches and multi-agent game theory to solve the problems of multi-agent resource allocation in dynamic systems. Using the quantum concept of superposition and entanglement, the framework can explore the solution spaces in parallel and therefore increase convergence speed and allocation efficiency.A quantum game-theoretic model is developed, which entails price-motivated system to control the strategic interaction of rational agents, in addition to maintaining stability, autonomy and equilibrium. The resource allocation problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solved with the Quantum Approximate Optimization Algorithm (QAOA) algorithm. The proposed approach is proved to be useful as simulation results show significant gains to classical baselines in terms of utility gain, fairness, and system throughput. The results highlight the possibilities of the quantum-game-theoretic paradigm as a fundamental engine of next-generation computing infrastructures, including networked cloud, edge and IoT with networked computing nodes in VPN-enabled networks.
Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.
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.