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Quantum AI is the use of quantum computing for computation of machine learning algorithms. Thanks to computational advantages of quantum computing, quantum AI can help achieve results that are not possible to achieve with classical computers.

Quantum mechanics is a universal model based on different principles than those observed in daily life. A quantum model of data is needed to process data with quantum computing. Hybrid quantum-classical models are also necessary in quantum computing for error correction and correct functioning of the quantum computer.

Quantum data: Quantum data can be considered as data packets contained in qubits for computerization. However, observing and storing quantum data is challenging because of the features that make it valuable which are superposition and entanglement. In addition, quantum data is noisy, it is necessary to apply a machine learning in the stage of analyzing and interpreting these data correctly.

Hybrid quantum-classical models: It is highly possible to obtain meaningless data only when using quantum processors to generate quantum data. For this reason, a hybrid model emerges when it is powered by fast data processing mechanisms such as CPU and GPU, which are frequently used in the classical computer.

Quantum algorithms: An algorithm is a sequence of steps that leads to the solution of a problem. In order to execute these steps on a device, one must use specific instruction sets that the device is designed to do so. Quantum computing introduces different instruction sets that are based on a completely different idea of execution when compared with classical computing. The aim of quantum algorithms is to use quantum effects like superposition and entanglement to get the solution faster.