 Hands-on workshop for optimization with quantum annealing and quantum-inspired technologies: From QUBO to black-box problems. Date: Sunday, 13 September 2026 | Description This hands-on workshop introduces optimization using quantum annealing and quantum-inspired technologies, with a focus on practical implementation that can be directly applied to real-world computational science and engineering problems. Participants will learn the basics of QUBO/Ising formulation, implement and execute optimization workflows in Python, and then extend them to black-box optimization problems, commonly arising in simulation- and experiment-driven applications such as design optimization and materials discovery. In addition, we will introduce applications such as aerodynamic optimization using CFD-based simulations and hyperparameter tuning in machine learning, with an emphasis on workflows that remain effective on classical hardware while being compatible with quantum technologies.Target audience Researchers, students, and practitioners in computational science and engineering, optimization, numerical simulation, and related fields, as well as those interested in quantum annealing and quantum-inspired technologies for practical applications in research and industry. Expected pre-knowledge Basic Python knowledge is sufficient. No prior knowledge of quantum computing is required, and optimization concepts will be introduced during the workshop. |
 Quantum error correction (QEC) and the interface of QEC. Date: tbd | Description This workshop will discuss quantum error correction (QEC) and the interface of QEC with quantum applications. Participants will learn the basics of QEC, and how compilation of algorithms works, from a logical circuit to gates executed on a fault-tolerant quantum computer. The workshop will also cover the loading of classical data, especially for Partial Differential Equations, into a logical program. Loading of data is a bottleneck for lots of applications on fault-tolerant devices, so this will have broad applicability. |
Bridging Classical CFD and Quantum Computing via the Lattice Boltzmann Method Date: tbd | Description Numerical simulation of turbulent fluid dynamics remains a grand challenge due to a lack of scale separation; atmospheric flows can span seven orders of magnitude, making classical resolution prohibitively expensive. This tutorial introduces an emerging framework that bridges direct numerical simulation and quantum computing via the Lattice Boltzmann Method (LBM). Unlike standard Navier-Stokes equations that struggle with Re-driven nonlinearities, LBM utilizes a mesoscopic particle-distribution evolution where nonlinearities are locally restricted. Participants will explore Carleman linearization to recast the LBE into linear systems suitable for Quantum Linear System Algorithms (QLSA), potentially achieving exponential speedup with costs scaling at O(log N). The session concludes with a hands-on component using the QURI SDK to implement quantum LBM workflows and hybrid HPC-quantum pipelines.Target audience Researchers, computational engineers, and students interested in fluid dynamics, kinetic theory, and the transition from classical CFD to fault-tolerant quantum algorithms Expected pre-knowledge Familiarity with fluid dynamics (Navier-Stokes) and basic linear algebra is recommended. Basic Python knowledge is required for the hands-on SDK session. |
Scaling engineering simulations with quantum-inspired methods Date: tbd | Description Quantum technologies offer an entirely new paradigm within which to perform physics simulations. While quantum computing is still a long-term prospect, quantum-inspired methods, which have long been state-of-the-art for simulations of quantum physics, are recently finding success for other types of simulation, most notably for computational fluid dynamics. These methods promise significant memory and runtime reductions for large-scale simulations of physics via PDE-solving, unlocking modelling capabilities that have so far remained out of reach of conventional approaches. In this tutorial we will give an introduction to tensor network methods in the context of PDE solving. We will explain why the approach makes sense and what we can expect to gain from it, as well as showcasing some recent results obtained by Fermioniq in collaboration with Deltares and NLR. Participants will get some hands-on experience with Fermioniq's PDE software library developed for rapid prototyping and implementation of models, with GPU and tensor network acceleration supported out of the box. Target audience Engineers, researchers and students active in the computational sciences and engineering, with an interest in partial differential equations and quantum-inspired methods. Expected pre-knowledge Familiarity with python is required. Some previous exposure to methods for solving partial differential equations is nice to have. |
