Quantum Neural Networks (QNNs) are a theoretical framework that combines principles of quantum computing with artificial neural networks to potentially process and learn from vast amounts of data more efficiently than classical systems.
Current limitations in classical computing for handling large datasets and complex models, leading to slow training times and high computational costs.
QNNs utilize the superposition and entanglement properties of qubits, allowing them to represent multiple states simultaneously. This enables parallel processing on a scale not achievable by classical computers, which can significantly speed up learning processes in machine learning tasks.
Theoretical; no practical manufacturing process exists yet due to the immature state of quantum hardware technology.
Requires development of quantum algorithms tailored for neural network architectures, which is currently a research challenge. Also involves creating qubits with sufficient coherence time and error correction capabilities.
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