Algorithmic Strategies for seizing Quantum Computing

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.