Quantum gradient descent is a quantum computing-based optimization method that leverages the principles of quantum mechanics to find the minimum of a function efficiently. This approach utilizes quantum parallelism to evaluate gradients, potentially speeding up convergence in machine learning tasks compared to classical methods. By integrating this technique with various machine learning paradigms, it can enhance supervised learning, unsupervised learning, and reinforcement learning frameworks.
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