  There is much debate on whether quantum computing on current NISQ devices,
consisting of noisy hundred qubits and requiring a non-negligible usage of
classical computing as part of the algorithms, has utility and will ever offer
advantages for scientific and industrial applications with respect to
traditional computing. In this position paper, we argue that while real-world
NISQ quantum applications have yet to surpass their classical counterparts,
strategic approaches can be used to facilitate advancements in both industrial
and scientific applications. We have identified three key strategies to guide
NISQ computing towards practical and useful implementations. Firstly,
prioritizing the identification of a "killer app" is a key point. An
application demonstrating the distinctive capabilities of NISQ devices can
catalyze broader development. We suggest focusing on applications that are
inherently quantum, e.g., pointing towards quantum chemistry and material
science as promising domains. These fields hold the potential to exhibit
benefits, setting benchmarks for other applications to follow. Secondly,
integrating AI and deep-learning methods into NISQ computing is a promising
approach. Examples such as quantum Physics-Informed Neural Networks and
Differentiable Quantum Circuits (DQC) demonstrate the synergy between quantum
computing and AI. Lastly, recognizing the interdisciplinary nature of NISQ
computing, we advocate for a co-design approach. Achieving synergy between
classical and quantum computing necessitates an effort in co-designing quantum
applications, algorithms, and programming environments, and the integration of
HPC with quantum hardware. The interoperability of these components is crucial
for enabling the full potential of NISQ computing.
