How to Handle Classical Data in Quantum Models

Quantum Machine Learning (QML) combines quantum computing with machine learning, with hybrid quantum-classical models being the most common approach. A central challenge in this field is encoding classical data into quantum states for processing by a quantum computer. The primary workflows involve using classical data with quantum models, which requires an encoding step, or the less common approach of using quantum data directly with quantum models. Key encoding techniques include Basis Encoding, which maps binary data directly to qubits, and Angle Encoding, which uses quantum gate rotations to represent continuous data features.