Call for Use Cases

As of September 1, 2026, QUICS is inviting companies to submit industrial problems for exploration on hybrid classical and quantum hardware. No in-house quantum expertise or hardware investment is required; in fact, the call is aimed at companies without quantum computing expertise. In particular, use cases in the field of machine learning, optimization, and simulation of chemistry systems or materials are suitable.

Selected submissions will be developed by the QUICS team from the initial problem statement through to the finished solution. Companies can choose to what extent they will be involved in the developing of their use case, but a minimum level of consulting (and possibly data) is required.

Applicants should fill in the short form and submit it to be considered. This includes a short written description covering the problem itself, its business relevance, and how it is currently addressed.

Is my problem a good fit?

Quantum computers offer up to exponential advantages for specific problems. Two questions will tell you whether yours is one of them.

1. Are there too many possible answers to check them all? Good candidates are problems where the number of options explodes: every route a fleet could take, every shape a molecule could fold into, every way to schedule a plant. Your engineers cannot test all of them today, so they test a few thousand and pick the best one found. Quantum computers are built for exactly this situation. Poor candidates are problems that are mainly about processing large amounts of stored data, such as searching years of sensor logs or images.
2. Is the answer you need short? A quantum computer produces its answer by being run many times and taking the most common result. A short answer is therefore cheap, and a long one is expensive. Good questions end in one route, one ranking, one design, one number, or a yes or no.

Examples

Let us consider a few examples of such use cases:

1. Planning and logistics. A delivery company has thousands of stops and wants the cheapest set of routes. The rules fit on one page. Checking every combination would take longer than the age of the universe. The answer wanted is a single plan.
2. Production scheduling. A factory assigns jobs to machines under constraints on staff, deadlines, and changeover times. Simple rules, an enormous number of valid schedules, one answer wanted.
3. Chemistry, materials, and pharma. Many questions here are comparisons: Is candidate A better than candidate B at binding, reacting, or conducting? Which of the two catalysts performs better? Is protein X better than protein Y at a given process? The molecule may be extremely complicated, but the question you need answered is short. Quantum computers suit this because the underlying chemistry is quantum to begin with. Today’s machines cannot yet beat a good classical method on these problems. What they can do is run a scaled-down version of your real question end to end, on a workflow that grows with the hardware.

The pattern in all of them: simple rules, a huge number of possibilities, and a short answer. Companies that build that workflow now will be ready when the hardware reaches the size where it pays off.

Technical details

For technically minded readers: the relevant property is that the problem is specified compactly (a Hamiltonian, a cost function, a constraint set) while the solution space it generates grows exponentially. If the problem can be specified compactly and also involves a very large amount of input data, however, one has to keep in mind that loading that data into a quantum state is itself costly.

Why now, when quantum computers are still small?

Because the work of turning your problem into something a quantum computer can run is the slow part, and it is hardware-independent. Hardware is improving quickly. Companies that have already framed their problem, built the workflow, and tested it at a small scale will be able to run at a useful size as soon as the machines allow. This call is about building that head start, not about beating your current solver next year.

Participants can expect:

• To own the developed solution to their problem
• Access to QUICS’ quant-design platform (powered by GWDG’s new agentic AI science environment (AScE)) yielding access to a range of quantum and classical hardware.
• Guidance through each stage of the process, from framing the problem to building and testing an algorithmic approach.
• Long-term value, with results that companies can build on for future applications or development.

An academic cloud account is required to submit a use case proposal. If you do not have an account yet, you can easily create one.

Please submit your application, including a description of your problem, by October 31, 2026.