  Quantum computing is a nascent technology with prospects to have a huge
impact in the world. Its current status, however, only counts on small and
noisy quantum computers whose performance is limited. In this thesis, two
different strategies are explored to take advantage of inherently quantum
properties and propose recipes to seize quantum computing since its advent.
First, the re-uploading strategy is a variational algorithm related to machine
learning. It consists in introducing data several times along a computation
accompanied by tunable parameters. This process permits the circuit to learn
and mimic any behavior. This capability emerges naturally from the quantum
properties of the circuit. Second, the unary strategy aims to reduce the
density of information stored in a quantum circuit to increase its resilience
against noise. This trade-off between performance and robustness brings an
advantage for noisy devices, where small but meaningful quantum speed-ups can
be found.
