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This advancement in quantum algorithms could help accelerate some of the most computationally intensive simulations used in engineering today. Helsinki, Finland - April, 2, 2026 Researchers from Quanscient, a leader in cloud-based multiphysics simulation technology and quantum algorithms, and Haiqu, a leading developer of quantum middleware, today announced a new algorithm that can significantly …

Quanscient, Oxford Ionics, and Airbus collaborate to explore quantum computing for computational fluid dynamics

Quanscient, Oxford Ionics, and Airbus collaborate to explore quantum computing for computational fluid dynamics

Inside Quanscient’s quantum team: A summer of learning, challenges, and growth

If you look at what is happening with AI right now in industries like law, medicine, or software development, the contributions are massive. But those industries all share one underlying trait: the data they process is primarily text-based or code-based. AI is an expert at handling that. Hardware engineering is different. Historically, we have relied on siloed, proprietary CAD and computer-aided …

Key takeaways - AI-driven simulation enables engineers to explore far more design possibilities than traditional simulation workflows. - Surrogate models make it possible to evaluate thousands of design configurations in milliseconds. - Visualizing the full design space helps engineers better understand trade-offs between competing performance objectives. - Faster simulations allow teams to exper…

Key takeaways - Quantum computing does not yet replace classical HPC in engineering simulation, but it is driving meaningful research into new computational methods and hybrid workflows - The most realistic near-term value lies in algorithm development, benchmarking, and quantum-ready simulation strategies rather than immediate performance advantage - Aerospace, maritime, and automotive industrie…

What is inside? This book outlines how MultiphysicsAI can drive measurable business impact by modernizing engineering workflows and business decision-making in fast growing industries, such as automotive, aerospace, consumer devices, and medical devices. Who is this for? MultiphysicsAI e-book presents a practical path from traditional digital product development to scalable, democratized multiphy…

Key takeaways - Nonlinear multiphysics simulation captures harmonic distortion in MEMS microspeakers. - Harmonic balance analysis enables efficient frequency-domain THD computation. - Large-scale cloud-based data generation supports AI surrogate model training. - Pareto front analysis identified designs achieving 30% higher SPL with similar THD. - Quanscient Allsolve provides a unified platform f…

Technical contributors Dr. Ljubomir Budinsky, Dr. Çaǧlar Aytekin, Dr. Valtteri Lahtinen Key takeaways - Physics-Informed Neural Networks offer a flexible way to solve PDEs, but scalability remains a challenge - Separable PINNs reduce the dimensionality burden, yet dense matrix operations still dominate cost - Quantum Orthogonal SPINNs use quantum circuits as orthogonal quantum layers in the netwo…

Key takeaways - Nonlinear systems are common in real-world applications and are often hard to analyze with traditional transient methods, which can be slow and noisy. - The harmonic balance method provides a faster, more accurate way to analyze these systems by breaking their behavior into simple, periodic patterns, including those caused by system complexity. - Cloud-based multiphysics simulatio…

Key takeaways - Inverse problems help estimate material properties by minimizing the difference between simulations and experimental data. - With an API-driven workflow, Quanscient Allsolve automates this process, reducing manual work and speeding up optimization. - Quanscient Allsolve runs simulations in the cloud with parallel computing, significantly reducing processing time. - The optimized m…

Key takeaways - Quanscient Allsolve MultiphysicsAI integrates high-performance multiphysics simulation with AI to accelerate inverse design. - Thousands of simulations and a highly accurate surrogate enable near-instant exploration of design trade-offs. - Engineers gain clear visibility into feasible performance boundaries and maintain full control over design choices. - AI-generated results are …

Key takeaways - MultiphysicsAI integrates neural surrogate models with conventional multiphysics solvers to accelerate engineering design workflows. - Neural surrogates allow rapid evaluation of large numbers of design configurations at minimal computational cost. - Candidate designs identified by surrogates are verified with full solvers to ensure physical accuracy. - The approach enables explor…

Key takeaways - Finite element simulations provide accurate and valuable insights into the electromagnetic behavior of grounded coplanar waveguides (GCPWs). - Careful design and optimization of GCPW geometry are essential for achieving desired performance characteristics. - Quanscient Allsolve's cloud-based multiphysics simulation platform significantly accelerates complex electromagnetic simulat…

Guest Author Pietro Zanotta Quantum Engineering Intern Introduction Last summer, Pietro Zanotta joined Quanscient as a Quantum Engineering Intern. At the time, he had just completed his bachelor’s degree and was preparing to start his PhD, making the internship a natural transition point in his academic and professional journey. During his time at Quanscient, Pietro worked with the Quantum Algori…

Key takeaways - Nonlinear multiphysics simulation captures harmonic distortion in MEMS microspeakers. - Harmonic balance analysis enables efficient frequency-domain THD computation. - Large-scale cloud-based data generation supports AI surrogate model training. - Pareto front analysis identified designs achieving 30% higher SPL with similar THD. - Quanscient Allsolve provides a unified platform f…

Quanscient has released MultiphysicsAI, a new approach that turns high-quality simulation data into AI-driven insights, allowing engineers to explore design options faster and more confidently. By generating proprietary datasets from Quanscient Allsolve simulations, the system enables AI models to map trade-offs and identify the most promising designs across complex engineering spaces. Traditiona…

Introduction Engineering has always been about translating abstract goals into real-world performance. Whether the task is to design a more efficient motor, a more sensitive ultrasonic sensor, or a lighter aircraft structure, engineers are essentially solving inverse problems: they know the target behavior they want, but not the exact design that will achieve it. Traditional simulation tools were…

Most teams still run simulations to check what one design will do. But product decisions aren’t made one design at a time, they’re made across trade-offs, timelines, and constraints. The missing piece has been fast, trustworthy data to illuminate the entire design space, not just a handful of points. Today, we’re changing that. The problem we kept hearing Traditional tools tell you what a design …