The Center for Data Innovation recently spoke with Juha Riippi, CEO of Quanscient, a Finland-based startup developing an AI-powered engineering-simulation platform that combines cloud and quantum computing. Riippi explained how the company’s technology helps engineers simulate complex physical systems, explore more design options, and accelerate product development across industries ranging from semiconductors to aerospace.
David Kertai: What does Quanscient offer?
Juha Riippi: Engineering teams today face a fundamental bottleneck. They work with increasingly complex physical systems, yet many design tools still rely on computing power and software architectures that cannot efficiently handle today’s most demanding engineering problems. As a result, companies in semiconductors, energy, automotive, aerospace, and other advanced industries struggle to evaluate large design spaces, test many design options, and understand how different variables affect performance. This slows research and development, increases reliance on physical prototypes, and limits innovation.
Quanscient addresses this challenge with a cloud-based engineering platform that allows engineers to design, simulate, and optimize complex systems before building physical prototypes. The platform combines multiphysics simulation—which models several physical processes, such as electricity, heat, mechanics, and fluid flow at the same time—with high-performance computing and machine learning. Engineers can run thousands of simulations in parallel, generate large physics-based datasets, and build AI models that predict performance much faster than running a full simulation every time.
Kertai: How does your platform simulate complex physical systems?
Juha Riippi: Engineers begin by creating a virtual model of the product or system they want to design. They import the geometry from computer-aided design software, assign material properties, and define operating conditions such as electrical currents, temperatures, mechanical forces, or fluid flow. The platform then solves the underlying physics equations to predict how the system will perform under real-world conditions.
Instead of evaluating a single design, engineers can vary nearly every design parameter and automatically run thousands of simulations across many configurations. These simulations generate large physics-based datasets that train AI models, which approximate the results of full simulations in a fraction of the time. This allows engineers to explore more design options, identify optimal configurations, and better understand how design changes affect performance.
Kertai: How do you combine quantum and classical computing to run simulations?
Juha Riippi: Today, all simulations on our platform run on cloud-based classical computers. Quantum computing will initially complement those simulations by solving the most computationally demanding parts of specific engineering problems. Our workflow follows four steps: simulate the system, train machine-learning models, explore the design space, and validate the most promising designs using high-accuracy simulations. Engineers can automate each step through our software-development kit, while generative AI models help create simulation setups and configure solver parameters.
The first area where quantum computing could have a major impact is computational fluid dynamics, which models how liquids and gases move around objects. Simulating airflow around an aircraft, for example, requires calculations that exceed the practical limits of today’s classical computers. We have already demonstrated that quantum computers can solve small versions of the equations that govern fluid flow. As quantum hardware matures, we expect quantum computing to become another simulation engine within our platform, working alongside classical cloud computing to solve increasingly complex engineering problems
Kertai: What types of industries or applications benefit most from your technology?
Juha Riippi: Semiconductor companies already use our platform to simulate devices such as accelerometers and gyroscopes found in smartphones and other electronics. These components involve complex interactions between electrical and mechanical forces, and our platform allows manufacturers to simulate large numbers of devices simultaneously while accounting for interference between nearby components.
Engineers also use the platform to design electric motors, where large-scale three-dimensional simulations often exceed the capabilities of traditional software. We also work with fusion-energy developers to model the superconducting magnets that confine plasma inside experimental fusion reactors. Looking ahead, we expect the platform to expand across aerospace, automotive, maritime, and defense applications, particularly those involving computational fluid dynamics.
Kertai: What is your long‑term vision for Quanscient and the future of quantum-powered engineering?
Juha Riippi: Our long-term goal is to make advanced engineering simulation more accessible and scalable by combining AI, cloud computing, and quantum computing in a single platform. As quantum hardware matures, engineers will be able to solve problems that are currently beyond the reach of even the world’s fastest supercomputers while exploring far more design possibilities.
We believe this shift will fundamentally change how companies develop new technologies. By making engineering simulation faster, more accurate, and easier to scale, we hope to help industries build safer, more efficient, and more sustainable products while accelerating innovation across a wide range of fields.
