Finding the Right Problems: Identifying Practical Quantum Use Cases
One of the most common questions organizations face when exploring quantum computing is simple: Which problems might benefit from quantum approaches?
While quantum algorithms have shown promise in several areas, identifying meaningful industrial use cases remains a complex task. The challenge is not only technological but also conceptual. It involves translating real-world scientific or industrial problems into computational structures that quantum algorithms can address.
Understanding computational bottlenecks
Many industrial research workflows already rely heavily on advanced computing. Fields such as pharmaceuticals, materials science, and energy research frequently combine simulation, data analysis, and machine learning to solve complex problems.
In this context, quantum exploration often begins with identifying where classical computation struggles most.
Examples include:
- Molecular simulations that grow exponentially with system size
- Optimization problems with extremely large search spaces
- Complex physical models that require high computational precision
Understanding these bottlenecks is often the first step toward evaluating potential quantum approaches — but navigating this landscape is rarely straightforward.
This challenge of identifying suitable problems is widely recognized across the quantum ecosystem. As one practitioner noted, "that is the real bottleneck today: we are stuck between having abstract algorithms and needing concrete, classically-hard problem instances that actually map to the real world."
Reformulating the problem
Once a computational challenge is identified, researchers must determine whether it can be mapped to known quantum algorithms. This may involve reformulating the problem mathematically, simplifying certain aspects, or isolating specific subproblems.
"In practice, algorithmic mapping involves identifying the specific computational bottleneck within a classical workflow and matching it against established quantum primitives," explains Karim Essafi, Director of Research and Technology at QunaSys Europe. "These primitives are then adapted to the constraints of the target hardware, ensuring that the quantum circuit remains feasible while preserving the potential for a computational advantage."
In many cases, only a portion of a larger workflow may be suitable for quantum computation. Hybrid quantum-classical approaches often emerge from this process.
Lessons from early exploration
Organizations that have begun exploring quantum computing frequently report that the process itself leads to deeper insights into their computational challenges.
Early projects are often less about immediate performance gains and more about building understanding and internal capabilities. As reflected across the industry, these efforts should not be judged purely on short-term return on investment, but rather as part of a broader capability-building process.
Even when quantum advantage remains a long-term goal, the process of identifying candidate use cases can help teams better understand the structure of their problems and the limits of current computational methods.
For many companies, this exploratory phase represents an important step in preparing for the future of advanced computing.