  Several benchmarks have been proposed to holistically measure quantum
computing performance. While some have focused on the end user's perspective
(e.g., in application-oriented benchmarks), the real industrial value taking
into account the physical footprint of the quantum processor are not discussed.
Different use-cases come with different requirements for size, weight, power
consumption, or data privacy while demanding to surpass certain thresholds of
fidelity, speed, problem size, or precision. This paper aims to incorporate
these characteristics into a concept coined quantum utility, which demonstrates
the effectiveness and practicality of quantum computers for various
applications where quantum advantage -- defined as either being faster, more
accurate, or demanding less energy -- is achieved over a classical machine of
similar size, weight, and cost. To successively pursue quantum utility, a
level-based classification scheme -- constituted as application readiness
levels (ARLs) -- as well as extended classification labels are introduced.
These are demonstratively applied to different quantum applications from the
fields of quantum chemistry, quantum simulation, quantum machine learning, and
data analysis followed by a brief discussion.
