Engineering Simulation

Accelerate fluid dynamics and multiphysics simulation with quantum computing

Engineering simulation is at the heart of modern product development. From designing more efficient aircraft and electric vehicles to optimizing turbines, reactors and industrial processes, simulation enables engineers to reduce physical testing, improve performance and shorten development cycles.

As products become more complex, however, classical high-performance computing (HPC) is reaching practical scaling limits. Quantum computing, combined with HPC and AI, offers a new path toward solving the most computationally intensive simulation problems with greater fidelity and efficiency.

Who this applies to

This solution is particularly relevant for organizations in:

• Aerospace and defense
• Automotive and motorsport
• Energy and utilities
• Oil and gas
• Process industries
• Industrial equipment manufacturing
• Marine engineering
• Engineering software providers
• National HPC centers

The challenge

Modern engineering increasingly depends on high-fidelity simulation.

Whether modeling airflow over an aircraft wing, cooling inside a data center, combustion inside a turbine or fluid flow through industrial equipment, engineers rely on computational fluid dynamics (CFD) and multiphysics simulations to make critical design decisions.

However, increasing simulation accuracy comes at a steep computational cost.

Engineering teams must constantly balance:

• Simulation accuracy
• Computing time
• HPC resource availability
• Project deadlines
• Development costs

As models become more detailed, simulation time grows dramatically, forcing engineers to rely on approximations that can impact downstream design decisions.

Business impact

Quantum-enhanced engineering simulation has the potential to deliver measurable value throughout the product development lifecycle.

Potential business outcomes include:

• Faster engineering design cycles
• Reduced physical prototyping
• Improved aerodynamic performance
• Lower HPC infrastructure costs
• Shorter time-to-market
• Better energy efficiency
• Increased product reliability
• More accurate digital twins
• Higher engineering productivity

Evaluate your engineering simulations for quantum advantage

Discover where quantum computing can enhance your existing HPC and AI simulation workflows.

→ Speak with our Quantum Engineering Team

Why classical simulation reaches its limits
Many engineering problems are governed by nonlinear partial differential equations (PDEs).

Although today’s CFD software delivers remarkable results, solving these equations accurately becomes increasingly difficult as simulation complexity grows.

Several challenges drive this computational bottleneck.

Turbulent flow
Accurately resolving turbulence requires computational resources that quickly become impractical for industrial-scale simulations, leading engineers to rely on turbulence models and approximations.

Nonlinear systems
Many real-world engineering systems exhibit chaotic or nonlinear behavior that demands costly iterative numerical methods.

Mesh resolution
Increasing mesh density improves simulation accuracy but causes computational cost to grow rapidly, often making the highest-fidelity simulations prohibitively expensive.

Multiphysics interactions
Modern products combine fluid dynamics, heat transfer, structural mechanics and electromagnetics within a single simulation, significantly increasing computational complexity.

AI-enhanced simulation
Machine learning accelerates simulation workflows, but its predictive accuracy remains fundamentally limited by the quality of the simulation data used for training.

The quantum approach
Quantum computing complements rather than replaces existing engineering simulation tools.

Hybrid quantum-classical workflows allow the most computationally demanding portions of PDE and CFD calculations to be offloaded to quantum processors while conventional HPC continues handling the remainder of the workflow.

As fault-tolerant quantum computers mature, advanced quantum algorithms are expected to address classes of simulation problems that become prohibitively expensive for classical architectures alone.

Organizations investing in quantum readiness today will be positioned to integrate these capabilities as they become commercially viable.

Representative use cases

Aerospace aerodynamics
Improve the simulation of airflow around aircraft, launch vehicles and drones to optimize lift, drag, fuel efficiency and stability while reducing dependence on wind tunnel testing.

Automotive engineering
Accelerate aerodynamic optimization for electric and conventional vehicles, improving energy efficiency, cooling systems and overall vehicle performance.

Energy systems
Optimize fluid flow in turbines, heat exchangers, reactors and renewable energy systems to improve operational efficiency and reduce energy losses.

Industrial process engineering
Model complex mixing, combustion and chemical processing systems with greater fidelity to improve product quality, process stability and resource utilization.

Digital twins
Support next-generation digital twins capable of simulating complex physical systems in near real time using more accurate computational models.

Assess your simulation roadmap
Our quantum application engineers can evaluate your computational workflows and identify where hybrid quantum simulation can create measurable engineering value.

→ Book a technical assessment

Technical deep dive

Many engineering simulations ultimately require solving large systems of partial differential equations.

For linear systems arising from PDE discretization, quantum algorithms—including the Harrow-Hassidim-Lloyd (HHL) algorithm and its successors—offer asymptotic scaling advantages for specific classes of problems under well-defined conditions. These theoretical results establish that certain linear system solvers belong to problem classes where quantum computation can provide structural computational advantages.

For nonlinear systems, ongoing research extends these concepts through hybrid quantum-classical methods, quantum encoding techniques and quantum algorithms for stochastic nonlinear PDEs.

Representative research directions include:

Quantum Lattice Boltzmann Methods (QLBM)
QLBM naturally maps lattice Boltzmann simulations onto quantum processors, enabling highly compact encoding of computational grids. Recent demonstrations have successfully reproduced large-scale turbulent flow phenomena while pointing toward future industrial applications as fault-tolerant hardware becomes available.

Nonlinear stochastic PDEs
Emerging quantum algorithms show promising approaches for solving classes of nonlinear stochastic systems—including forms of the Navier-Stokes equations—that remain computationally challenging for classical numerical methods.

As logical qubit counts continue to increase, these approaches are expected to become increasingly relevant for industrial engineering simulation.