Optimize global supply chains in real time with Wipro’s hybrid quantum-powered solutions
Global supply chains are increasingly complex and fragile. Enterprises juggle thousands of suppliers, fluctuating demand, transportation constraints, and sustainability goals, all under pressure from geopolitical disruptions and climate events. Classical optimization techniques, such as linear programming or heuristic solvers, struggle when the problem scales to millions of variables and constraints. Real-time decision-making becomes nearly impossible, leading to inefficiencies, higher costs, and reduced resilience.
Why Quantum Computing:
Supply chain optimization is inherently a combinatorial problem e.g. finding the best configuration of routes, inventory levels, and production schedules under multiple constraints. Quantum computing, particularly algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing, excels at such problems. These algorithms leverage quantum superposition to explore vast solution spaces simultaneously, identifying near-optimal solutions faster than classical methods.
The Quantum Advantage:
- Speed and Scale: Quantum algorithms can handle large-scale optimization problems that classical solvers cannot, enabling near real-time recalibration of supply chains.
- Cost Efficiency: By optimizing routes and inventory dynamically, enterprises can reduce transportation costs and minimize stockouts or overstock situations.
Our Solution
We developed a quantum prototype for supply chain optimization using QAOA on a hybrid quantum-classical framework. The solution modelled a multi-level supply chain with constraints on capacity, lead times, and cost. We integrated quantum algorithms with classical pre-processing to reduce problem complexity and executed the optimization on a quantum simulator and scaled it up leveraging QPUs available on AWS Braket.
