🧠 Quantum Neural Networks
Merging quantum computing with machine learning
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0 / 5 completed🤖 The Quantum-Classical Convergence
Quantum Neural Networks (QNNs) combine quantum computing's exponential power with neural networks' learning capabilities. By encoding data in quantum states and using parameterized quantum circuits as trainable layers, QNNs can represent complex functions in exponentially smaller parameter spaces—potentially revolutionizing AI.
💡 Why Quantum Neural Networks?
Classical neural networks with N neurons can represent 2^N states, but need exponential parameters. Quantum networks encode N qubits in 2^N dimensional Hilbert space naturally—exponential representational capacity with linear resources. A 20-qubit QNN can represent patterns requiring 1 million classical neurons.
🎯 What You'll Learn
📊 Classical vs Quantum Neural Networks
🔬 Key Insight: Quantum Advantage
QNNs excel at problems with quantum data (molecular dynamics, quantum sensors), high-dimensional patterns (feature interactions in exponential space), and structured data (graphs, sequences with entanglement). The advantage comes from representing correlations classical networks need exponential parameters to capture.