data-encoder

# Data Encoder ## Purpose Provides expert guidance on encoding classical data into quantum states for machine learning applications, balancing expressiveness with circuit complexity. ## Capabilities - Angle encoding - Amplitude encoding - IQP encoding - Hardware-efficient encoding - Encoding expressibility analysis - Data re-uploading strategies - Feature scaling for encoding - Encoding depth optimization ## Usage Guidelines 1. **Feature Analysis**: Understand data dimensionality and structure 2. **Encoding Selection**: Choose encoding based on data type and qubit budget 3. **Scaling**: Apply appropriate normalization for encoding method 4. **Depth Analysis**: Balance encoding expressivity with circuit depth 5. **Verification**: Validate encoded states capture relevant features ## Tools/Libraries - PennyLane - Qiskit Machine Learning - Cirq - TensorFlow Quantum - NumPy