Quanscient, a Tampere-based startup specialising in cloud-based multiphysics simulation technology and quantum algorithms, today announced a €10 million Series A funding round to accelerate its international expansion.
The round was led by Danish quantum fund 55 North and Austrian industrial investor B&C Group, with full re-participation from existing investors Maki.vc, Crowberry Capital, QAI Ventures, and First Fellow Partners.
“AI will not transform hardware engineering unless simulation itself is rebuilt for it. By making multiphysics code-driven and cloud-scalable, we generate the volume of physics data that AI needs, turning simulation from a bottleneck into the engine of data-driven design. This brings to hardware engineering the same shift AI has delivered for software,” Quanscient co-founder and CEO Juha Riippi said.
Quanscient was founded by Juha Riippi, Alexandre Halbach, Asser Lähdemäki, and Valtteri Lahtinen in 2021, and Andrew Tweedie joined as the fifth founder in 2024.
The company claims to be the first cloud-and-quantum-powered multiphysics simulation software provider with the goal of transforming R&D with accurate digital prototypes.
The Finnish startup states that although AI has transformed nearly every field, hardware engineering remains constrained. Engineers still rely on complex, slow, trial-and-error processes. According to Quanscient’s recent study, 89% of engineers routinely simplify their physics models just to fit within runtime budgets. Current AI models are unable to accurately replicate real-world physics and lack sufficient data to learn how it works. Consequently, simulation continues to be a significant bottleneck in engineering.
Quanscient claims to address this challenge by building physics simulation as code-driven, cloud-scalable, and built to generate the volume of multiphysics data that AI needs to learn from. The company notes that its platform enables faster, higher-quality product development and accelerates time-to-market across industries, including energy, aerospace, and automotive.
The company also enables fully digital product development and testing, reducing the need for expensive physical prototypes. It allows engineers to evaluate multiple design options early, reducing risk and improving the final product’s performance. The platform delivers simulations up to 100× faster, cutting runtimes by up to 99%. Quanscient points out that AI integration further enhances design by identifying optimal trade-offs, accelerating the path from concept to implementation, and revealing otherwise hidden solutions.
“Industrial competitiveness depends on both speed and accuracy. The architecture we’ve built for cloud and quantum simulation is also the foundation for an entirely new category of AI and will enable the physics-aware AI models that hardware engineering has been waiting for,” added Riippi.
Quanscient’s global team comprises 40 experts from 15 countries. Major industrial firms in Europe, North America, and Japan, including Fortune 100 companies, rely on Quanscient to accelerate and enhance their R&D efforts.
Helmut Katzgraber, Chief Science Officer and General Partner at 55 North, said, “Engineering teams are under pressure to explore much larger design spaces and more complex physics than legacy tools were built for. Quanscient’s cloud‑native multiphysics platform, combined with forward-looking work on quantum algorithms and AI tools, gives customers a future-proof step‑change in throughput without sacrificing accuracy.
“We believe this capability will be critical for innovators in areas like nuclear fusion, advanced electronics, and quantum technologies, and we are thrilled to back the team on this journey.”
With this fresh funding, the company plans to accelerate its international expansion and advance the development of the market’s first platform that unifies simulation, quantum algorithms, and AI integration.
Read the orginal article: https://www.eu-startups.com/2026/05/finlands-quanscient-raises-e10-million-to-scale-its-multiphysics-simulation-platform-for-the-ai-era/



