🔄 Quantum Data Encoding
Transform classical data into quantum states
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0 / 5 completed🌟 The Bridge to Quantum States
Quantum data encoding transforms classical information into quantum states—the first critical step in any quantum algorithm. A vector x = [0.5, 0.3, 0.7, 0.2] becomes |ψ⟩ = 0.5|00⟩ + 0.3|01⟩ + 0.7|10⟩ + 0.2|11⟩. Encoding efficiency determines quantum advantage—logarithmic qubit usage versus linear classical bits.
💡 Why Encoding Matters
Classical data lives in bits—0s and 1s. Quantum algorithms need quantum states in superposition. Poor encoding wastes qubits (basis encoding: n features = n qubits). Smart encoding exploits amplitude encoding: n features = log₂(n) qubits—exponential compression! For 1024 features: 1024 classical bits vs 10 qubits.
🎯 What You'll Learn
📊 Encoding Efficiency Comparison
Most efficient—exponential compression
Linear scaling—one feature per qubit
Least efficient—direct binary mapping
🔬 Key Insight: The Encoding Tradeoff
Amplitude encoding is most efficient but requires O(n) gates to prepare n amplitudes. Angle encoding uses more qubits but only O(n) gates with simple rotations. Basis encoding wastes qubits but prepares instantly. Choose encoding based on your hardware constraints and data structure.