Hybrid quantum-classical approaches are methods that combine the strengths of both quantum and classical computing to solve complex problems more efficiently. These approaches leverage the unique capabilities of quantum systems, such as superposition and entanglement, while utilizing classical algorithms and computational power for tasks that are better suited to classical processing. This synergy allows for enhanced performance in various applications, particularly in fields like optimization, machine learning, and quantum kernel estimation.
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