In the chemicals and materials sector, efficiency isn’t just a metric, it’s a competitive advantage. From production lines to global supply chains, every process must balance complexity, cost, and constant change. But traditional computing systems often hit a wall when faced with the scale and variability of real-world operations.
Quantum computing offers a new approach. By solving complex optimisation problems far beyond the reach of classical systems, quantum algorithms are helping manufacturers improve throughput, reduce waste, and respond more nimbly to shifting market conditions.
Case: Process optimisation with quantum computing
Chemical manufacturing involves thousands of interconnected variables: raw material availability, equipment capacity, maintenance schedules, shipping constraints, energy use, safety requirements, and more. Optimising across all of these dimensions, in real time, is one of the industry’s most persistent challenges.
Traditional optimization tools can’t always handle this scale of complexity. That’s where quantum computing steps in. Quantum algorithms, particularly in combinatorial optimization, can evaluate exponentially more possibilities than classical methods. This allows them to:
- Optimise scheduling across entire production networks
- Balance resource allocation dynamically
- Improve supply chain routing and inventory forecasting
- Respond to real-time data and disruptions with adaptive process control
These insights help scientists design and validate new catalysts digitally, before entering costly pilot or production phases.
Instead of static planning, quantum-enabled optimization makes agility a core capability, helping chemical manufacturers stay efficient, resilient, and competitive in a volatile global landscape.
Business value
- Increased production efficiency and yield
By optimising schedules, inputs, and process flows, manufacturers can run closer to peak performance.
- Reduced operational costs and downtime
Fewer delays, better coordination, and smarter maintenance planning lead to significant cost savings.
- Enhanced supply chain resilience
Quantum models can adapt to disruptions more effectively, from raw material shortages to logistics delays.
- Greater agility in a dynamic market
Real-time optimisation enables faster responses to changing demand, regulation, or energy pricing.
Technology readiness
Quantum-powered optimisation is one of the most mature applications in the field. Pilots and proofs-of-concept are already being deployed, often using hybrid quantum-classical workflows that combine classical data systems with quantum solvers for the most complex subproblems.
These projects are showing early success in areas like scheduling, warehouse management, and logistics. As quantum hardware improves in scale and stability over the next five to seven years, industrial-scale adoption will accelerate, particularly in sectors like chemicals, where even small efficiency gains can yield massive value.
Leading players and experiments
Dow and BASF have launched pilot projects using quantum platforms to streamline production workflows and resource planning.
D-Wave and IBM are offering cloud-based quantum optimisation tools, enabling manufacturers to test use cases like energy efficiency and predictive maintenance.
Mitsubishi Chemical and Siemens are experimenting with quantum-enhanced process control systems, aiming to improve agility and decision-making on the factory floor.
Discover more use cases here.
