Ricerca CERCA

Quantum machine learning


Description Quantum Machine Learning (QML) is a promising cutting-edge field of research. It will allow for faster training times and unlock new possibilities for both classical machine learning approaches and deep learning. QML needs Quantum Computers able to efficiently exploit the quantum mechanical properties of qubits (the counterparts of classical bits). Each Qubit has the ability to carry more information with respect to the classical bit since it can exist in a Superposition: it can be in an intermediate state which is probabilistically both 0 and 1 at the same time. This property (among others) makes possible the so-called Quantum speedup that can be extremely relevant and cause a vast outperforming of classical architectures.

While many efforts are being done to design and improve QML techniques, current technological limitations and the need for a computational paradigm shift mean that a lot of work is still needed to achieve practical algorithms, able to outperforms classical ones at scale.

The aim of this study is to make a further step in this field by studying the current state of the art and the possibility of improving currently existing approaches. The focus is the creation of a practical quantum algorithm, which would represent a step towards the broader introduction of such methods. This achievement would have relevant implications for the continuous improvement of quantum computing, with potential benefits for a plethora of different application fields, such as finance, chemistry and telecommunications.




Deadline 21/10/2023      PROPONI LA TUA CANDIDATURA

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