<ul><li>Neural networks (NNs) have driven significant advances in machine learning, but often require large numbers of trainable parameters.</li><li>Variational quantum circuits (VQCs) offer a promising alternative as they require fewer parameters and leverage quantum mechanics.</li><li>A study evaluated NNs and VQCs on simple supervised and reinforcement learning tasks with different parameter sizes.</li><li>Results showed that VQCs can match NNs in performance, despite longer training durations.</li></ul>

Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits

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