Optimizing complex supply chain networks using quantum computational methods
Keywords:
Quantum Optimization, Supply Chain Networks, Stochastic Modeling, QAOA, Computational AdvantageAbstract
This paper presents a quantum-enhanced framework for optimizing complex supply chain networks (SCNs) under stochastic demand, addressing the computational limitations of classical optimization methods. We propose a hybrid quantum–classical approach that integrates the Quantum Approximate Optimization Algorithm (QAOA) with hierarchical graph decomposition, enabling scalable optimization of networks with more than 100 nodes on NISQ-era hardware.
The framework introduces three major contributions:
- A quantum-native method for modeling stochastic demand using amplitude estimation, providing quadratic speedup over Monte Carlo simulation;
- A decomposition strategy that coordinates quantum subproblems through an augmented Lagrangian formulation; and
- An error-adapted QAOA scheme with zero-noise extrapolation to improve quantum resource utilization.
Experimental results show that the proposed method achieves a 42% reduction in computation time compared with classical solvers while maintaining solution quality within 4.7% of optimality. The approach demonstrates near-linear scalability and superior performance in volatile-demand, high-connectivity scenarios.
Overall, this work highlights the potential of quantum computing as a practical tool for real-time SCN optimization and sets the foundation for future extensions involving multi-objective models and quantum machine learning integration.
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