🤖 Quantum Machine Learning
Merge quantum computing with AI for exponential advantages
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🤖 The Quantum-AI Convergence
Quantum Machine Learning (QML) combines quantum computing's exponential state spaces with AI's pattern recognition power. The result: exponential speedups for training, inference, and data encoding—unlocking capabilities impossible for classical ML.
💡 Why QML Matters
Classical ML struggles with high-dimensional data, complex feature spaces, and exponential model parameters. Quantum computers naturally operate in exponentially large Hilbert spaces, making them ideal for ML tasks that scale poorly classically.
Classical parameters for n features:O(2n)
Quantum parameters for n qubits:O(n)
🎯 What You'll Master
🧠
Quantum Neural Networks
Parameterized quantum circuits as neurons
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Quantum Kernels
Feature maps in Hilbert space
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Hybrid Models
Classical-quantum integration
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Real Applications
Image recognition, NLP, finance
⚡ Classical ML vs QML
🖥️Classical ML
Training time:O(n3)
Feature space:Linear
Kernel trick:Polynomial
Max features:~1000
⚛️Quantum ML
Training time:O(log n)
Feature space:Exponential
Kernel trick:Quantum
Max features:2n (unlimited)
🎯 Core QML Algorithms
Quantum SVMExponential
Classification
Accuracy: 95%
Quantum Neural NetPolynomial
Deep learning
Accuracy: 92%
Quantum PCAExponential
Dimensionality reduction
Accuracy: 98%
Quantum GANQuadratic
Generative modeling
Accuracy: 88%