Variational quantum algorithms are a class of quantum algorithms that utilize a combination of quantum and classical computing techniques to find approximate solutions to complex optimization problems. These algorithms typically employ a parameterized quantum circuit, which is optimized using classical methods to minimize a cost function, often related to the problem being solved. This makes them particularly useful in areas such as machine learning, finance, and scientific simulations.
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