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Quantum computing is a relatively young technology, which promises to revolutionize computation in the coming years. Interest in this technology has been increasing in recent years, both in research communities and in industry.
A certain number of physical implementations of quantum computers have been proposed and developed by different companies (e.g. IBM, Google, D-Wave, etc.), mainly for two paradigms of this technology, gate-based quantum computing and quantum annealing. The former has analogies with classical computers, in the fact that qubits (quantum bits) are tranformed by means of quantum logic gates. The latter, instead, exploits the tendency of a physical system to maintain its state of minimum energy, in order to solve appropriately formulated optimization problems.

A lot of research, both theoretical and practical, has been done in the field, showing how quantum computers could overpower classical ones.
However, being this a new and still growing technology, it comes with some constraints. For example, limits on the number of working qubits and connectivity among them pose a challenge in solving real-world problems.

Therefore, the objective of this research is to assess the capabilities of current quantum computing technologies and prepare in advance for future developments by means of classical and hybrid solutions. In particular, the focus is given to applications of quantum computing in machine learning, a broad field that is founded on intensive tasks and optimization problems which could take advantage of quantum technologies.