  Quantum computing has made considerable progress in recent years in both
software and hardware. But to unlock the power of quantum computers in solving
problems that cannot be efficiently solved classically, quantum computing at
scale is necessary. Unfortunately, quantum simulators suffer from their
exponential complexity and, at the same time, the currently available quantum
computing hardware is still rather limited (even if roadmaps make intriguing
promises). Hence, in order to evaluate quantum computing applications,
end-users are still frequently restricted to toy-size problem instances (which
additionally often do not take error correction into account). This
substantially hinders the development and assessment of real-world quantum
computing applications. In this work, we demonstrate how to utilize Resource
Estimation to improve this situation. We show how the current workflow (relying
on simulation and/or execution) can be complemented with an estimation step,
allowing that end-users (1) actually can consider real-world problem instances
already today (also considering error correction schemes and correspondingly
required hardware resources), (2) can start exploring possible optimizations of
those instances across the entire design space, and (3) can incorporate
hypotheses of hardware development trends to derive more informed and, thus,
better design space parameters. Overall, this enables end-users already today
to check out the promises of possible future quantum computing applications,
even if the corresponding hardware to execute them is not available yet.
