  Quantum computing uses the physical principles of very small systems to
develop computing platforms which can solve problems that are intractable on
conventional supercomputers. There are challenges not only in building the
required hardware, but also in identifying the most promising application areas
and developing the corresponding quantum algorithms. The availability of
intermediate-scale noisy quantum computers is now propelling the developments
of novel algorithms, with applications across a variety of domains, including
in aeroscience. Variational quantum algorithms are particularly promising since
they are comparatively noise tolerant and aim to achieve a quantum advantage
with only a few hundred qubits. Furthermore, they are applicable to a wide
range of optimization problems arising throughout the natural sciences and
industry. To demonstrate the possibilities for the aeroscience community, we
give a perspective on how variational quantum algorithms can be utilized in
computational fluid dynamics. We discuss how classical problems are translated
into quantum algorithms and their logarithmic scaling with problem size. As an
explicit example we apply this method to Burgers' Equation in one spatial
dimension. We argue that a quantum advantage over classical computing methods
could be achieved by the end of this decade if quantum hardware progresses as
currently envisaged and emphasize the importance of joining up development of
quantum algorithms with application-specific expertise to achieve real-world
impact.
