Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we use a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical Newsvendor Problem known to be NP-hard. Such problems provide the building block for supply chain management, particularly in inventory management and procurement optimization under risks and uncertainty.
Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we use a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical Newsvendor Problem known to be NP-hard. Such problems provide the building block for supply chain management, particularly in inventory management and procurement optimization under risks and uncertainty.
In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate analytically, and thus, is approximated using simulation techniques such as Monte Carlo simulation. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation. Hence, in many cases, it can achieve a speed-up for simulation-based optimization as well. Combining QAE with ideas from quantum combinatorial optimization, we show how this can be used not only for continuous but also for discrete optimization problems. Furthermore, the algorithm is demonstrated on illustrative problems such as portfolio optimization with a Value at Risk constraint and inventory management.
In the post-pandemic world, manufacturing enterprises face increasing uncertainties, especially with vulnerabilities in global supply chains. Although supply chain management has been extensively studied, the critical influence of decision-makers (DMs) in these systems remains underexplored. This study studies the inventory management problem under risk using the newsvendor model by incorporating DMs risk preferences. By employing the Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) algorithm, the estimation of probabilities or expectation values can be done more efficiently. This offers near-quadratic speedup compared to classical Monte Carlo methods. Our findings illuminate the intricate relationship between risk-aware decision-making and inventory management, providing essential insights for enhancing supply chain resilience and adaptability in uncertain conditions
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
A quantum-enabled blockchain architecture for secure and efficient supply chain management is created by combining quantum optimization, decentralized transaction control, and post-quantum security enforcement. Supply chain decision factors such as supplier allocation, routing optimization, and inventory coordination are framed as combinatorial optimization problems and solved using the Quantum Approximate Optimization Algorithm. The optimum decision outputs are carried out via blockchain-based smart contracts, which enable automatic validation, transparent processing, and immutable transaction storage. A quantum-resistant security layer allows for safe authentication, encrypted communication, and attackresistant transaction verification against both classical and quantum adversaries. A multi-tier simulated supply chain environment is used to evaluate system performance, which includes measures such as latency, throughput, optimization accuracy, security resilience, and operational cost. When compared to traditional supply chain and blockchain-based implementations, the results show faster convergence, lower transaction latency, more security, and greater transparency. The findings support the efficacy of combining quantum optimization, decentralized ledgers, and quantum-resilient encryption for next-generation intelligent supply chains.
Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by IBM Qiskit and the qBraid system.
This article presents a quantum-enhanced framework for optimizing complex supply chain networks (SCNs) under stochastic demand, addressing computational intractability in classical approaches. We propose a hybrid quantum-classical methodology integrating the Quantum Approximate Optimization Algorithm (QAOA) with hierarchical graph decomposition, enabling industrial-scale optimization (?100 nodes) on NISQ-era hardware. Key innovations include: (1) First quantum-native stochastic demand modeling via amplitude estimation, achieving quadratic speedup (O(log(1/?)/?)) over Monte Carlo methods; (2) A novel decomposition algorithm coordinating quantum subproblems through augmented Lagrangian optimization; (3) Error-adapted QAOA with zero-noise extrapolation, improving quantum volume utilization to 85%. Empirical validation demonstrates 42% faster computation versus classical solvers (CPLEX) while maintaining solution quality within 4.7% of optimality. The framework exhibits near-linear scalability (efficiency = 0.92) and outperforms heuristics in high-connectivity, volatile-demand scenarios. Mathematical contributions include a stochastic Hamiltonian formulation for normally distributed demand and noise-aware parameter optimization. This work establishes quantum computing as a viable tool for real-time SCN optimization, particularly for multi-period networks with uncertainty. Future research will explore multi-objective extensions, quantum machine learning integration, and dynamic reconfiguration protocols.