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In this episode, we break down what Quantum Neural Networks (QNNs) actually are and why they might eventually reshape the future of AI. QNNs combine quantum mechanics with classical neural architectures, replacing traditional neurons with qubits that can exist in multiple states at once. This gives them an extraordinary representational advantage: through superposition and entanglement, QNNs can model complex correlations and nonlinear functions in ways that classical networks simply can’t.

But today’s reality is more grounded. Because quantum hardware remains in the noisy, error-prone NISQ stage, QNNs are typically built as Hybrid Quantum–Classical (HQC) systems, where a quantum circuit performs transformations and a classical optimizer trains it. The biggest technical barrier is the Barren Plateaus problem, where gradients vanish exponentially as circuits deepen, making training brutally difficult.

We explore how researchers are working to overcome these limits — and what QNNs could unlock once quantum hardware matures.

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