Quantum Feature Map

A **Quantum Feature Map** is a process that converts classical input data into a **quantum state** using a parameterized quantum circuit. This transformation encodes data into the **high-dimensional Hilbert space** of a quantum system, allowing quantum algorithms to exploit complex correlations not easily captured classically. Essentially, it’s the quantum analogue of feature mapping in machine learning, used to make data more separable for classification or regression tasks.