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In the last decade, machine learning (ML) techniques have become essential to find patterns in big data and to mine valuable information. Quantum computing is a promising approach to train and exploit ML models, potentially in a fraction of the time and cost, without the need for power-consuming high-performance computing and data centers. However, quantum computers are currently in an early stage of technological development, limited by hardware engineering constraints. Several quantum computer technologies and computational architecture paradigms exist, each characterized by different trade-offs in terms of the number of qubits, fidelity, and other metrics.
