🎨 Quantum Generative Models
Creating synthetic data with quantum computing
Your Progress
0 / 5 completed🌟 Quantum Creativity
Generative models learn to create new data samples from training distributions—images, molecules, text. Quantum generative models leverage superposition and entanglement to represent and sample from exponentially complex probability distributions, potentially creating synthetic data classical models cannot.
💡 Why Quantum Generation?
Classical GANs struggle with mode collapse and require millions of parameters. Quantum circuits naturally represent probability distributions via Born's rule: P(x) = |⟨x|ψ⟩|². A 10-qubit circuit encodes 2¹⁰ = 1024 probabilities; 20 qubits = 1 million. Quantum sampling provides exponential capacity with polynomial resources.
🎯 What You'll Learn
📊 Generative Paradigms
🔬 Key Insight: Born's Rule
Quantum states |ψ⟩ define probability distributions via P(x) = |⟨x|ψ⟩|²—Born's rule. By training quantum circuits to prepare states matching target distributions, we create generative models naturally. Sampling from |ψ⟩ is exponentially easier than computing all probabilities classically.