Machine Learning Modeling of a Quantum Algorithm Generator for State Preparation
With the rapid development of quantum computers, different quantum algorithms have been designed to solve specific problems.
Many of these algorithms require an additional preliminary stage which consists in encoding classical data into quantum states, with a process called state preparation. With the increasing number of qubits of quantum devices, exact methods for state preparation do not scale well and, in the future, alternative solutions must be found. Approximate techniques of state preparation exist, but finding the most suited to encode a given set of data is not straightforward.
The aim of this project is to develop a machine learning model that generates the most suited quantum algorithm which performs effectively approximate state preparation and scales well with respect to the number of qubits.
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