Hybrid quantum-classical algorithms combine the strengths of classical computing and quantum computing to tackle complex problems more efficiently. They leverage quantum processors for specific tasks that can benefit from quantum speedup, while using classical systems for parts of the computation that are better suited to traditional methods. This approach helps in optimizing the overall performance and resource usage, making them particularly relevant in areas like neural networks, practical implementations, and distributed quantum machine learning.
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