The provided transcript excerpt frames quantum computing as a radically different computing model whose value is not making today’s software run incrementally faster, but unlocking specific problems that conventional computers cannot realistically solve. After a brief introduction, the moderator positions the panel as a group of European quantum specialists and asks them to make the topic tangible: what their companies build, what problems they address, and how quantum fits into the broader category of radical breakthroughs. Anne-Marije describes Groove Quantum as building semiconductor-based quantum computers in the Netherlands, using germanium rather than today’s silicon-dominated computing stack. Jan introduces IQM as a full-stack quantum computer builder using superconducting materials on silicon wafers, selling early systems to universities, research centers, supercomputing centers, and cloud users. Toby Cubitt introduces Phasecraft as a quantum algorithms company focused on making useful applications run on emerging quantum hardware.

The panel then clarifies one of the session’s central points: asking how much faster quantum computers are than classical computers is, in Toby Cubitt’s words, “exactly the wrong question.” Quantum computers use a different mathematical model of computation. They are not a replacement for classical machines, nor a better way to run ordinary algorithms. Instead, their promise lies in problems such as quantum-level physical simulation, materials science, chemistry, early-stage biochemistry, drug discovery foundations, and certain optimization tasks. The speakers emphasize that modeling atoms, electrons, and nuclei scales exponentially on classical machines, quickly reaching impossible runtimes, whereas quantum computers can match the structure of inherently quantum problems.

The discussion becomes more concrete through examples and analogies. Jan describes a Volkswagen battery-development project in which most of a digital twin ran on conventional supercomputers, while a small but difficult strongly correlated electron problem was delegated to a quantum computer. Anne-Marije compares classical computing progress to moving from a bike to a car to a race car, while quantum computing is more like a plane: useful for certain destinations, inappropriate for others. Jan extends the analogy by noting that today’s error-prone quantum computers resemble water planes that can only land on lakes, raising the open question of whether future machines will address broad oceans of problems or remain specialized.

The final portion of the provided excerpt turns to ecosystem and business-model evolution. Jan explains that IQM initially imagined an “Intel of quantum” model focused on chipsets, but shifted toward full-stack systems because the surrounding integration ecosystem was not yet mature. Anne-Marije notes a similar industry pattern: chip suppliers exist, but without enough integrators, chips alone do not become computers. The panel compares this to early IBM-style full-stack classical computing before later compartmentalization into chip, integration, and software layers. The excerpt ends as Toby begins discussing Phasecraft’s dependence on external hardware companies, so any later conclusions, Q&A, or broader radical-breakthrough outlook are not available in the supplied transcript.