March 19, 2026 -- How can complex industrial challenges be tackled through the interplay of classical and quantum computing? Researchers at Saarland University, together with the quantum computing start-up planqc and industry partners BMW and Infineon, are bringing both worlds together. Their shared objective is to develop new approaches to complex optimization problems in order to make industrial processes more efficient. The project is funded with €2.3 million by the German Federal Ministry of Research, Technology and Space (BMFTR).

In industry—such as in the production and distribution of cars or semiconductor chips—highly complex mathematical problems arise that classical computers can still only solve approximately today, often requiring long runtimes and substantial computational resources. The algorithms used are typically inspired by real-world problems and exploit their structure heuristically. “This works remarkably well. Algorithms that are theoretically slower than others can nevertheless be faster in practice,” explains Peter P. Orth, Professor of Theoretical Physics of Quantum Information at Saarland University. Yet despite their quality, these approaches often represent only the best solution achievable under current conditions. For Peter P. Orth, his colleague Markus Bläser, Professor of Computer Science and expert in complexity theory and algorithms, the industry partners Infineon and BMW, and the quantum computing start-up planqc, this is not sufficient.

They are therefore pursuing new paths within the research project QIAPO – Quantum-Informed Approximate Optimization on NISQ and Partially Fault-Tolerant Quantum Computers. A dedicated neutral-atom quantum computer built by planqc in Garching will be used to “shrink” highly complex industrial problems to a level where classical computers and their proven algorithms can handle them more effectively. Quantum computers can outperform conventional computers in certain cases because their basic units, qubits, can exist in superpositions of the states 0 and 1, whereas classical bits can only be either 0 or 1. This makes quantum computers particularly well suited to solving or simplifying highly complex mathematical problems that would overwhelm classical systems.

“QIAPO shows not only how far quantum computing has already progressed. It also demonstrates today how industrially relevant problems can be translated into quantum algorithms—and ultimately tested on quantum computers,” said Dr. Martin Kiffner, Head of Algorithms at planqc.

Once a mathematical problem has been simplified using quantum computing researchers can continue working with the many successful and well-established classical algorithms to complete the computation. Even with this hybrid approach, fully exact solutions will often remain out of reach, which is why the project explicitly focuses on approximate optimization. The aim is to solve existing problems through a combination of quantum and classical algorithms slightly better than is currently possible.

To illustrate with a simplified example: if a problem can currently be solved with around 80% accuracy, a hybrid quantum–classical approach could enable an efficient improvement to 85% or even 95%. “This is where the quantum computer can step into the gap—improving accuracy and potentially delivering a quantum advantage,” explains Peter P. Orth.

The project follows a realistic and pragmatic goal. “We will not solve the biggest problems overnight within the next three years,” says Orth. “But by the end of the project, we will very likely know whether our approach can, in principle, tackle such problems—and we can then pursue this direction further.” Even small efficiency gains in industrial production and logistics can have a major impact: as stated in the project description, “even minor resource savings can lead to substantial financial effects at high production volumes.”