data-encoder

name	data-encoder
description	Classical data encoding skill for quantum machine learning applications
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"quantum-computing","domain":"science","category":"quantum-ml","phase":6}

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