Introduction
Innovation in computational modeling is at the heart of modern engineering. Whether designing a next-generation aircraft, optimizing a ship’s hull for reduced drag, or improving the aerodynamics of an electric vehicle, engineers rely on simulation to explore design alternatives, assess performance, and minimize physical testing.
Traditionally, these simulations have run on classical high-performance computing (HPC) infrastructures. However, as industrial systems become more complex and the demand for high-fidelity prediction grows, new computational paradigms are emerging, most notably quantum computing and quantum-enabled simulation algorithms.
This blog explores how quantum computing is opening new research directions in simulation, focusing on three key industrial domains as aerospace, maritime, and automotive. It addresses current capabilities, realistic near-term opportunities, and the integration of quantum-enhanced algorithms into engineering workflows, drawing on developments at Quanscient and broader research trends.
The classical challenge
Across high-tech engineering sectors, simulation serves as a cornerstone of design and validation.
- Aerospace engineers use Computational Fluid Dynamics (CFD) to understand airflow around wings, optimize fuel efficiency, and analyze complex phenomena such as shock interactions at transonic speeds.
- Maritime designers simulate fluid flows around ship hulls and propellers to reduce resistance and improve propulsion efficiency.
- Automotive teams rely on multiphysics simulations to assess vehicle aerodynamics, thermal management in electric vehicles, and noise, vibration, and harshness (NVH) characteristics for passenger comfort.
In each case, simulation solves large systems of nonlinear partial differential equations. When physics models grow in fidelity, coupling fluid mechanics with thermal, acoustic, or structural effects, the computational cost rises steeply. Classical solvers often require extensive HPC resources and long runtimes, limiting design space exploration and slowing development cycles.
This challenge has motivated research into alternative compute paradigms that could break through classical limits, including hybrid classical–quantum methods and quantum-native algorithms designed for future quantum hardware.
Realistic potential in engineering simulation
Quantum computers leverage principles of quantum mechanics, such as superposition and entanglement, to represent and process information in fundamentally different ways than classical systems. In theory, this could allow certain computations to scale more efficiently with problem size.
That said, a critical distinction is necessary. Current quantum hardware does not yet outperform classical HPC across general engineering simulation tasks. Practical quantum advantage, meaning a clear and broad performance benefit over classical methods, remains a mid-to-long-term goal. Instead, the focus today is on algorithm development, benchmarking, and pilot applications that anticipate future hardware improvements.
Quantum algorithms for CFD, for example, aim to reframe fluid dynamics problems in ways that exploit quantum resources for specific computational kernels. One approach developed by researchers is the Quantum Lattice Boltzmann Method (QLBM), which maps discrete fluid dynamics problems to quantum circuits.
These efforts are not speculative, they have produced world-first demonstrations of CFD on real quantum devices, such as multi-time-step QLBM simulations on a 50-qubit superconducting quantum computer. Although not yet ready to replace classical solvers, these results validate that quantum devices can execute meaningful physics simulations and provide a platform for systematic improvement as hardware matures.
Thus, the current and near-term reality of quantum computing in engineering is best framed as augmented simulation science, researching, developing, and benchmarking methods today that will unlock new capabilities tomorrow
Aerospace: Towards higher-fidelity aerodynamic modeling
Aerospace engineering depends heavily on accurate fluid dynamics simulation. Designers must reconcile aerodynamic efficiency, structural integrity, fuel economy, and safety across a wide range of flight conditions.
Classical challenges
- Capturing turbulence across scales
- Accurately modeling shock waves and boundary layers
- Coupling fluid dynamics with structural responses (fluid-structure interaction)
Classical CFD solvers have made remarkable progress. Nonetheless, the fidelity required for full-vehicle, flight-condition simulations remains computationally demanding.
Where quantum place a role
Quantum-enhanced algorithms aim to expand the scale and complexity of simulation problems that can be explored. In collaboration with partners such as Airbus and quantum hardware developers, researchers are testing quantum-compatible CFD formulations that could, in the future, enable:
- Finer discretization with reduced classical HPC overhead
- Faster iteration on candidate designs in early development stages
- Hybrid workflows where quantum devices handle specific bottleneck calculations while classical solvers manage the bulk of computation
Rather than replacing classical simulation, the value proposition today is co-developing domain-specific quantum algorithms that will be ready when hardware catches up. Aerospace companies that invest in this research are building internal expertise and benchmarks early, preparing for when practical advantage emerges.
Maritime: Improving hydrodynamics and energy efficiency
In the maritime industry, fluid interactions determine propulsion efficiency, hull performance, and environmental impact.
Simulation use cases
- Detailed modeling of hull resistance and wake flows
- Propeller cavitation prediction
- Hydrodynamics in complex sea states
- Fluid-structure coupling in offshore structures
Like aerospace, maritime simulations are constrained by computational resources, particularly for high-resolution unsteady flow problems.
Quantum's contribution
Quantum algorithms developed for CFD can extend to maritime applications because the underlying physics, Navier–Stokes equations and boundary layer effects, are common across fluids. Potential benefits include:
- Early adoption of quantum-compatible algorithmic frameworks that scale with mesh refinement
- Targeted quantum workflows for coupled phenomena, such as cavitation onset, where classical solvers struggle with scale
- Hybrid simulation pipelines that offload discrete heavy compute kernels to quantum processors in the future
This approach parallels developments in aerospace: investment in algorithm design today could yield practical tools as quantum hardware evolves.
The maritime industry’s long design cycles and conservative engineering culture make hybrid and incremental adoption particularly attractive, as companies can incorporate new algorithmic paradigms without compromising safety or certification.
Automotive: Aerodynamics, thermal management, and multiphysics optimization
Automotive simulation has broadened beyond aerodynamics to include multiphysics problems such as:
- Thermal behavior of electric vehicle battery systems
- NVH simulation for passenger comfort
- Cooling airflow through complex underbody geometries
Classical simulation tools are capable but often bottlenecked by multiphysics coupling and design iteration throughput.
Quantum-ready algorithmic strategies
In the automotive domain, quantum-compatible techniques might first find utility in optimization and parameter sweeps. Classical HPC excels at solving deterministic physics models, but design optimization across many variables remains costly. Quantum algorithms may, over time, offer:
- Novel approaches to high-dimensional optimization problems
- Efficient sampling methods in design space exploration
- Acceleration of specific phases in multiphysics simulation
Again, the most pragmatic near-term value lies in research and hybrid algorithm development rather than direct simulation replacement. Design teams that integrate quantum-aware workflows early can build comparative benchmarks and understand where quantum methods could reduce runtime or expand design coverage once hardware catches up.
Realistic integration: Hybrid workflows and quantum readiness
The emphasis across aerospace, maritime, and automotive sectors is not on if quantum will be applied but how organizations prepare for when it matures.
Hybrid classical–quantum workflows acknowledge that quantum devices will complement, not supplant, classical HPC for the foreseeable future. These workflows might involve:
- Using classical solvers for bulk computation
- Offloading specific kernels, such as linear system solves or discrete lattice steps, to quantum processors when beneficial
- Benchmarking quantum algorithms against classical baselines to quantify potential benefit curves
This integration requires investment in tooling, algorithms, and talent development—efforts akin to building internal expertise in parallel computing decades ago.
Supporting this transition are collaborative initiatives across industry and research institutions, where companies co-develop and evaluate novel quantum algorithms with academic and hardware partners.
Looking ahead: From development to adoption
The engineering simulation community is disciplined and evidence-motivated. Any new computational paradigm must demonstrate:
- Improved accuracy, reliability, or efficiency
- Compatibility with engineering validation and verification processes
- Clear value relative to cost and operational complexity
Quantum computing is progressing rapidly, but practical advantage in large-scale industrial simulation remains a multi-year horizon. The near-term opportunity lies in co-development of domain-specific quantum algorithms, hybrid workflows, and readiness frameworks that position organizations to leverage quantum computing as it matures.
Investments in quantum-enhanced simulation today, whether via collaborations with research groups or algorithm benchmarking projects, are analogous to investments in HPC and parallel computing from previous decades: they build internal capability ahead of broader industry adoption.
Conclusion
Engineering simulation is inherently about risk management, understanding how designs behave before committing to physical prototypes. Quantum computing introduces a new dimension to this landscape, not by promising instant breakthroughs, but by offering a systematic way to rethink computational limits.
Across aerospace, maritime, and automotive industries, the path forward involves:
- Identifying high-value simulation problems where classical methods face real bottlenecks
- Benchmarking quantum-compatible algorithms against classical baselines
- Developing hybrid workflows that integrate quantum methods into existing pipelines
- Investing in talent and partnerships that bring quantum research into engineering teams
This approach strikes a balance between pragmatism and foresight. It positions organizations to benefit from quantum computing as it matures, without overstating near-term capabilities.
By building readiness today, engineering teams can expand what is possible tomorrow, achieving deeper insights, exploring broader design spaces, and accelerating innovation across industries that underpin modern infrastructure and mobility.
Learn more about Quanscient Quantum Labs and get in touch now at quanscient.com/quantum
