Classical computers struggle to simulate manufacturing processes at the atomic or molecular level, because the number of variables grows exponentially with system size. Electrons interacting in molecules cannot be efficiently modelled with full accuracy on classical hardware.
Quantum computers, by contrast, naturally represent quantum states using quantum bits (qubits). This allows for the simulation of the electronic structure, which determines how a material reacts under heat or pressure, without having to build it in a lab. As such, quantum simulation does not just make modelling faster, it makes certain simulations feasible that are currently approximated or impossible.
Case: Process simulation with quantum capabilities
Manufacturing processes often involve complex chemical reactions, material transformations, and multi-step workflows. Understanding how materials behave under specific conditions is the key to success. Every reaction pathway, phase transition and molecular interaction influences yield, energy consumption and product quality.
Process simulation allows manufacturers to model these behaviours before physical production begins. It reduces uncertainty, shortens development cycles and limits costly experimentation. However, simulations at the atomic or molecular level are often infeasible for classical computers. As material systems become more complex (battery materials, semiconductors, carbon capture catalysts), classical approximation errors become economically relevant.
Quantum simulation operates according to the same physical principles that govern molecular systems and can therefore represent quantum states more naturally. Instead of approximating electron behaviour, quantum processors can directly model complex molecular interactions. This opens the door to:
- Precise reaction pathway analysis
- Accurate energy state calculations
- More reliable materials modelling
- Improved yields
- More energy-efficient production methods
Business value
- Accelerated innovation: Virtual prototyping replaces large portions of physical experimentation, enabling rapid prototyping and testing of new materials and processes digitally before committing to production.
- Cost savings: Reduced trial-and-error cycles lower material waste, laboratory costs and energy-intensive pilot runs.
- Sustainability: Optimised reactions operate at lower temperatures and pressures, reducing emissions and environmental footprint, and leading to greener, more efficient manufacturing techniques.
- Quality improvement: More precise modelling enhances process control, increases yield, improves product consistency, and reduces defects.
Technology readiness
Quantum simulation in manufacturing remains in the research and early-stage pilot phase. Current systems operate in the Noisy Intermediate-Scale Quantum (NISQ) era, meaning qubit counts are limited and error rates remain high.
Most progress today occurs in:
- Small-molecule proof-of-concept simulations
- Hybrid quantum-classical workflows
- Targeted optimisation tasks within larger simulation pipelines
Full-scale industrial deployment will require fault-tolerant quantum hardware. However, early partnerships between manufacturers, research institutes and quantum technology providers signal strong long-term commitment. As quantum simulation capabilities advance, adoption is expected to grow.
Quantum simulation will not replace classical modelling overnight. Instead, it will gradually expand what is computationally possible, shifting manufacturing from experimental iteration toward predictive design.
Leading players and experiments
BASF explores quantum algorithms to accelerate innovation in R&D, particularly in the fields of chemistry and materials science. The focus is on simulating molecules and chemical reactions that are too complex for classical supercomputers.
Dow collaborates with 1QBit to develop quantum computing tools for materials science, with the explicit goal of accelerating discovery of new chemicals and materials and speeding product and process development.
Merck uses quantum simulation through strategic partnerships to accelerate the discovery of new medicines and advanced materials. The focus here is on accurately calculating complex molecular interactions and optimising clinical trials that are unfeasible for classical computers.
IBM provides quantum platforms enabling manufacturing R&D activities in materials discovery, process control, and supply chain optimisation, simulating vast chemical spaces for better catalysts and batteries.
Microsoft offers Azure Quantum Elements, blending AI, HPC and quantum technologies for industrial chemistry such as battery and catalyst design through generative chemistry and hybrid simulations.
Cambridge Quantum (Quantinuum) develops advanced algorithms and the open-source software InQuanto, which is specifically designed to simulate complex molecular systems and chemical reactions on quantum hardware.
Zapata Computing applies industrial generative AI and quantum-inspired algorithms for manufacturing optimisation, such as BMW plant scheduling, which outperforms classical solvers in efficiency.
Discover more use cases here.
